Development and Initial Validation of the Respiratory Therapy Learning Needs Instrument (RT-LNI): A Competency-Based Self-Assessment Instrument

Development and Initial Validation of the Respiratory Therapy Learning Needs Instrument (RT-LNI): A Competency-Based Self-Assessment Instrument | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Initial Validation of the Respiratory Therapy Learning Needs Instrument (RT-LNI): A Competency-Based Self-Assessment Instrument Mishal Delma Dsouza, Leigh Powell, Amar Hassan Khamis, Nabil Zary This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9484867/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Respiratory therapists (RTs) need continuous professional development to stay competent in evolving clinical areas. However, there is no validated, professional-specific tool for systematically evaluating their learning needs in respiratory therapy. This study aimed to create and preliminarily validate the Respiratory Therapy Learning Needs Instrument (RT-LNI), a self-assessment based on key competency frameworks. Methods A two-phase cross-sectional study was carried out at three Dubai Health tertiary care centers. In the first phase, six expert RTs assessed 83 items across 13 competency domains using the content validity index (CVI) methodology. During the second phase, 58 licensed RTs were invited and 50 completed the refined 75-item instrument (response rate of 72.5%), along with a user evaluation survey completed by 45 participants. Psychometric evaluation included Cronbach's alpha and domain-level exploratory factor analysis (principal axis factoring with varimax rotation; promax sensitivity analysis). The number of factors was determined by parallel analysis. Subgroup comparisons employed Mann-Whitney U tests with Bonferroni correction. The reporting adhered to the STROBE checklist. Results Content validation demonstrated high validity, with an S-CVI/Ave of 0.90 (95% CI 0.86–0.94). After removing eight items, the instrument consisted of 75 items. Internal consistency ranged from acceptable to excellent (Cronbach's alpha 0.70–0.95; median 0.91). Domain-level factor analysis mostly indicated unidimensional structures (KMO 0.64–0.88; median primary factor loading 0.79), except in Emergency and Critical Care, which showed a two-factor structure—acute resuscitation versus prolonged management. Self-assessed competence was highest in core technical skills (mean 3.7–3.9/5, corresponding to Dreyfus "Proficient" stage) and lowest in evidence-based practice (2.4/5), sleep medicine (2.6/5), and pulmonary rehabilitation (2.7/5), at the "Advanced Beginner" stage. No significant subgroup differences emerged after Bonferroni correction, although the study was underpowered for small-to-medium effects. User feedback indicated strong acceptability, with a mean score of 4.26/5 (85.3%). Conclusions The RT-LNI demonstrated strong content validity, acceptable psychometric properties, and high user acceptability in this preliminary validation. As the first published, psychometrically evaluated, profession-specific learning needs assessment instrument for respiratory therapy, the tool fills a gap in the CPD infrastructure. The identified learning needs patterns have direct implications for targeted workforce development. Multi-site confirmatory validation with larger samples and convergent validity testing against objective measures are the next crucial steps. respiratory therapy learning needs assessment competency-based assessment professional development content validity self-assessment instrument development United Arab Emirates Figures Figure 1 Figure 2 BACKGROUND Respiratory therapy is a rapidly advancing healthcare field that is essential in managing both acute and chronic cardiopulmonary conditions in various clinical environments [ 1 – 3 ]. Since its inception, the profession has grown considerably, shifting from a primary emphasis on oxygen delivery to include sophisticated therapeutic techniques such as mechanical ventilation, non-invasive respiratory support, point-of-care diagnostics, and emergency critical care procedures [ 4 , 5 ]. Today, respiratory therapy is a well-established global profession, with recognized practitioners in North America, Europe, the Middle East, and the Asia-Pacific region [ 1 – 3 ]. Additionally, organizations like the American Association for Respiratory Care (AARC) and the Canadian Society of Respiratory Therapists (CSRT) have developed comprehensive competency frameworks to standardize practice [ 5 , 9 , 10 ]. However, the profession encounters significant challenges in keeping practitioners' skills current amid rapid technological progress and rising patient complexity [ 4 , 8 ]. Respiratory therapy practice is continually broadening, with RTs taking on roles in high-flow nasal cannula therapy, point-of-care ultrasound, ventilator liberation protocols, and interprofessional leadership [ 5 , 13 ]. This expansion necessitates the systematic identification and fulfillment of substantial learning needs through ongoing professional development (CPD) efforts [ 12 – 14 ]. Although learning needs assessment is crucial for effective CPD, a systematic review of PubMed, CINAHL, and Scopus conducted in January 2026 using terms like "respiratory therap*" AND "learning needs" OR "needs assessment" OR "competency assessment" AND "instrument" OR "tool" OR "validation" revealed no published, psychometrically validated, profession-specific learning needs Instruments for respiratory therapists. Current approaches mostly use generic surveys adapted from nursing and allied health fields or rely on cross-sectional snapshots not connected to validated competency frameworks [ 6 – 8 ]. A scoping review by Al-Ismail et al. found that most CPD learning needs Instruments across health professions are general, with none specifically designed and validated for respiratory therapy. This lack of validated instrumentation has notable practical implications. Without a specialized, competency-focused Instrument tailored to the profession, institutions struggle to systematically evaluate learning needs across various practice areas, make informed decisions about continuing professional development (CPD), or monitor workforce progress over time [ 12 , 14 ]. Clinically, unrecognized learning needs can lead to less effective respiratory care, slower implementation of evidence-based practices, and missed chances for quality enhancement [ 1 , 4 ]. The RT-LNI's development was guided by three complementary theoretical frameworks. Firstly, Kern's six-step model for curriculum development in medical education [ 15 ] offered the overall structure, emphasizing needs assessment as the initial step for creating targeted educational interventions. Secondly, Bradshaw's taxonomy of needs [ 25 ] influenced the design of the instrument by differentiating between normative needs (defined by expert standards), felt needs (perceived by practitioners), expressed needs (specific demands for training), and comparative needs (gaps compared to peers). The RT-LNI mainly measures felt needs through self-assessment, while its alignment with AARC and CSRT competency frameworks provides the normative benchmarks for interpreting self-perceived competence. Thirdly, the Dreyfus model of skill acquisition [ 26 ] shaped the five-level rating scale (Novice to Expert), offering practitioners clear behavioral anchors for self-evaluation. Choosing self-assessment as the main measurement method requires clear justification. In health professions, literature shows only weak to moderate correlation between self-assessed and objectively measured competence [ 27 , 28 ]. Systematic reviews reveal a pattern: lower performers often overestimate their skills, while higher performers may underestimate, a trend seen among medical students [ 40 ], residents [ 41 ], and clinicians [ 42 ]. This miscalibration varies by domain; procedural skills tend to be more accurately assessed than knowledge-based or non-technical areas [ 42 , 43 ], and it also depends on feedback availability and external benchmarks [ 44 , 45 ]. A recent scoping review indicates that despite these limitations, self-assessment remains widely used as an outcome measure in health education, emphasizing the importance of defining its purpose carefully [ 46 ]. The conceptual shift led by Eva, Regehr, and Sargeant [ 29 , 47 , 48 ] redefines self-assessment not as an isolated measure of competence accuracy but as a socially and contextually based process of self-judgment. This process becomes educationally valuable when it is guided by external standards, feedback, and reflection. Instead of questioning whether practitioners can precisely evaluate their competence in an abstract sense, the key issue is whether structured self-assessment within competency frameworks can assist in identifying learning needs and supporting self-regulated professional growth [ 46 , 49 , 50 ]. The RT-LNI embraces this approach of informed self-assessment: it does not aim to measure objective competence but to recognize perceived learning needs within the normative structure of established competency frameworks (AARC, CSRT/NCF), offering both self-assessment and external benchmarks to contextualize responses. Self-assessment remains the most practical method for large-scale, routine identification of learning needs in clinical environments, and it is well recognized as useful for guiding personalized CPD planning when properly framed [ 46 , 47 ]. The purpose of this study was to develop and perform an initial validation of the RT-LNI, a comprehensive, competency-based self-Instrument specifically designed for respiratory therapists. The instrument covers technical skills, non-technical competencies, and specialized practice areas based on the AARC and CSRT/National Competency Framework (NCF). This study presents the content validation, initial psychometric assessment, learning needs analysis, and user acceptability results from a pilot study conducted within Dubai Health, an integrated public academic health system. METHODS Study Design and Setting This study used a quantitative, descriptive, cross-sectional approach in two phases: initially, content validation and instrument refinement, followed by pilot testing, psychometric assessment, and evaluation of user acceptability. The research received approval from the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) Institutional Review Board (IRB reference 202400216). The reporting adhered to the STROBE checklist for cross-sectional studies (see Additional file 1) [ 58 ]. Data collection took place at three Dubai Health government tertiary care facilities: Rashid Hospital, Al Jalila Children's Hospital, and Latifa Hospital. Dubai Health is the first integrated academic health system in Dubai, comprising multiple hospitals, primary care centers, and academic institutions throughout the Emirate. After IRB approval in March 2025, the period from April to August 2025 was dedicated to recruiting expert panels, validating content, revising instruments, obtaining site-specific approvals, and setting up the electronic survey. Pilot data collection was conducted between September and October 2025. A participant flow diagram is shown in Fig. 1 . Phase 1: Tool Development and Content Validation Item generation for the RT-LNI was based on three sources: (1) the AARC Entry to Respiratory Therapy Practice 2030 competency framework [ 9 ], (2) the CSRT National Competency Framework (NCF) for Respiratory Therapists [ 10 ], and (3) peer-reviewed literature on RT skills and learning needs [ 1 , 4 , 5 ]. A mapping table showing how each RT-LNI item aligns with its source competency framework is available in Additional file 2. This process generated 83 candidate items, divided into 3 sections and 13 competency domains: Section 1: Technical Skills and Knowledge (including Respiratory Assessment and Diagnostics, Oxygen and Aerosol Therapy, Airway Management, Mechanical Ventilation, and Emergency and Critical Care); Section 2: Non-Technical Skills (covering Communication and Interpersonal Skills, Leadership and Professional Development, Ethical and Professional Practice, and Evidence-Based Practice and Research); and Section 3: Specialized Practice Areas (comprising Neonatal/Pediatric Care, Pulmonary Rehabilitation and Pulmonary Function Testing, Sleep Medicine, and Home Care). A panel of six respiratory therapy experts was intentionally selected. Criteria for inclusion were at least 10 years of professional experience in respiratory therapy, active engagement in clinical practice, teaching, or supervision, and familiarity with competency-based curricula. The panel featured a diverse range of backgrounds, including adult critical care, pediatric and neonatal respiratory care, clinical education, and supervisory roles. Experts evaluated each item on a 4-point Likert scale (1 = Strongly Disagree to 4 = Strongly Agree) to assess its relevance to contemporary respiratory therapy practices in Dubai Health. Ratings of 3 or 4 indicated endorsement of relevance, following standard CVI methodology [ 16 – 18 ]. The Item-level CVI (I-CVI) was determined by the proportion of experts rating an item as relevant. Scale-level CVI was calculated as S-CVI/Ave (the average of all I-CVIs) and S-CVI/UA (the proportion of items with universal agreement, I-CVI = 1.00). Items with an I-CVI below 0.78 were reviewed for possible revision or removal, as per established guidelines [ 16 , 17 ]. Bootstrap 95% confidence intervals for S-CVI/Ave were estimated using the bias-corrected and accelerated (BCa) method with 10,000 replications to assess sampling uncertainty. Additionally, experts provided qualitative feedback on each domain. Phase 2: Pilot Testing and Psychometric Evaluation A total of 80 licensed respiratory therapists from the three Dubai Health facilities were invited through institutional email. Of these, 58 (72.5%) completed the RT-LNI, and 45 of the 58 (77.6%) also participated in the user evaluation survey. Inclusion criteria included: (i) current licensure as a registered respiratory therapist in the UAE, (ii) at least one year of professional experience, and (ii) a minimum of 20 hours weekly in clinical practice. Exclusion criteria included roles solely administrative without clinical duties, as well as unlicensed students or trainees. The revised 75-item RT-LNI was administered using a 5-point competency rating scale based on the Dreyfus model of skill acquisition [ 26 ], where 1 = Novice (basic awareness, needing significant guidance), 2 = Advanced Beginner (can perform with regular supervision), 3 = Competent (works independently in standard situations), 4 = Proficient (adapts approach with minimal supervision), and 5 = Expert (demonstrates mastery and can teach others). The instrument also included 22 open-ended questions to gather qualitative insights into learning needs and priorities. The user evaluation survey measured five dimensions using a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree): clarity and usability (6 items), relevance of content (5 items), perceived value and effectiveness (5 items), feasibility (4 items), and overall satisfaction (3 items). Both instruments were distributed electronically through Microsoft Forms. Data Analysis Descriptive statistics, including means, standard deviations, and ranges, were computed for all items and domains. Internal consistency was evaluated using Cronbach's alpha for each domain and for the entire instrument, with 95% confidence intervals derived through the Fisher transformation method. Standard benchmarks guided interpretation: alpha values of 0.70 or higher are acceptable, 0.80 or higher are good, and 0.90 or higher are excellent [ 59 ]. Corrected item-total correlations within each domain were analyzed to identify potentially redundant or underperforming items, with those below 0.30 flagged for further review. Construct validity was evaluated using domain-level exploratory factor analysis (EFA). The adequacy of factor analysis cannot be judged solely by fixed sample size cutoffs or simple subject-to-variable ratios; instead, it depends on the strength and clarity of the underlying structure, including communalities, factor loadings, and the number of indicators per factor [ 32 , 33 , 34 ]. Given the pilot sample size (n = 58), conducting a full 75-item EFA would have resulted in an unfavorable ratio of sample size to model complexity. Therefore, EFA was performed separately within each competency domain—a practical pilot-stage approach that minimizes dimensional complexity and assesses the internal consistency of each theoretically defined domain [ 35 , 36 ]. This method is appropriate for initial instrument development as a preliminary structural review, while recognizing that within-domain analyses cannot identify cross-domain item complexity and do not replace subsequent item-level validation [ 36 , 37 ]. Subject-to-variable ratios for each domain analysis ranged from 4.1:1 to 14.5:1. Principal axis factoring (PAF) with varimax rotation was used. The selection of orthogonal rotation was based on the instrument's design assumption that competency domains are conceptually distinct constructs; a sensitivity analysis using oblique (promax) rotation was also performed to evaluate solution stability given observed inter-domain correlations. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of sphericity were calculated for each domain. Factor retention was primarily guided by parallel analysis, supplemented by the Kaiser criterion (eigenvalue greater than 1.0) and scree plot review, in line with current methodological recommendations [ 38 , 39 ]. Factor loadings of 0.40 or higher were deemed acceptable, with loadings above 0.70 considered strong [ 59 ]. Complete factor loading matrices, communalities, and corrected item-total correlations for all 13 domains are provided in Additional file 3. These exploratory findings should be viewed as preliminary; confirmatory factor analysis with a larger independent sample is needed to confirm the instrument's definitive factor structure. Inter-domain correlations (Spearman's rho) were calculated to evaluate discriminant validity; correlations below 0.85 indicate that the constructs are distinct. Missing data were reviewed at both the item and the respondent levels. All 58 participants in Phase 2 completed all 75 Likert-scale items; no item-level missing data were found, as the electronic survey (Microsoft Forms) mandated responses to all competency items before submission. The user evaluation survey was optional; 45 of 58 participants (77.6%) completed it, and all 45 provided responses to every item. Therefore, no imputation or handling of missing data was needed. The 13 non-respondents to the evaluation survey are recognized as a potential source of non-response bias (see Limitations). Data distribution was evaluated for normality using the Shapiro-Wilk test. Subgroup comparisons based on years of experience (1–10 vs. 11 or more years) and prior use of learning needs Instruments (yes/no) were performed with Mann-Whitney U tests. Since there were 26 planned comparisons (13 domains by 2 grouping variables), a Bonferroni correction was applied (adjusted significance threshold: p less than 0.002). Although Bonferroni is more conservative compared to alternatives like the Holm-Bonferroni step-down procedure or Benjamini-Hochberg false discovery rate control, it was chosen for its simplicity and transparency in this exploratory study, where minimizing Type I error was prioritized. Both unadjusted and Bonferroni-adjusted p-values are presented to help readers evaluate practical significance alongside statistical significance. Effect sizes for Mann-Whitney U tests were reported as rank-biserial correlations (r), interpreted as: small (0.10), medium (0.30), large (0.50) [ 30 ]. A post-hoc sensitivity analysis showed that with n = 58 and alpha = 0.002, the smallest detectable effect size at 80% power was r = 0.52, indicating only large effects could be detected with the current sample. This reports the minimum detectable effect size rather than observed post-hoc power, aligning with recommendations that observed power calculations are uninformative because they are monotonically related to p-values [ 57 ]. This conservative approach was used to minimize Type I error in this exploratory analysis; however, the study was underpowered to detect small-to-medium subgroup differences, and null results should be interpreted accordingly. User evaluation data were analyzed descriptively. Open-ended responses were examined using summative content analysis: responses were read independently by two investigators (MDD, NZ), and recurring themes were identified through frequency counts. Themes mentioned by 25% or more of respondents were reported. Data were collected using Microsoft Forms, and all quantitative analyses were performed with IBM SPSS Statistics version 26.0. RESULTS Phase 1: Content Validation The expert panel included 6 respiratory therapists (4 male, 2 female) with an average of 18.3 years of professional experience (SD 4.2). Panel members came from diverse national backgrounds and had been practicing in the UAE for an average of 11.6 years (SD 3.8). Five experts (83.3%) mainly worked in adult critical care, two (33.3%) also had pediatric/neonatal experience, and one had emergency medicine experience. Three (50%) held formal roles as educators or supervisors, and four (66.7%) held master’s degrees or higher. Content validity analysis of the initial 83 items showed the following distribution: 54 items (65.1%) had an I-CVI of 1.00 (universal agreement), 16 items (19.3%) had an I-CVI between 0.83 and 0.99, and 13 items (15.7%) had an I-CVI below 0.78. Of these 13 items, 8 were permanently removed due to lack of consensus or redundancy. The remaining 5 items (I-CVI 0.67–0.78) were significantly revised based on expert qualitative feedback. Scale-level content validity was excellent: S-CVI/Ave = 0.90 (95% CI 0.86–0.94), surpassing the recommended threshold of 0.83 [ 16 – 18 ]. The S-CVI/UA was 0.65 (54 of 83 items with perfect agreement), a level consistent with validation studies involving six or more expert raters [ 18 ]. Domain-level CVI results are shown in Table 1 . Expert feedback prompted several key refinements: the inclusion of lung ultrasound as an emerging skill in respiratory assessment, expansion of non-invasive ventilation content, and enhancement of ethics items related to end-of-life care. Items concerning sleep medicine received lower I-CVI scores (range 0.67–0.83); however, these were kept due to the growing importance of sleep medicine in respiratory therapy and potential bias from panel composition, since all experts mainly practiced in critical care settings. The updated instrument now includes 75 items across 13 domains. Table 1 Content Validity Index Results by Domain Domain Items I-CVI Range Mean I-CVI UA n (%) Resp. Assessment & Diagnostics 8 0.83-1.00 0.96 6 (75.0) Oxygen & Aerosol Therapy 7 0.67-1.00 0.88 4 (57.1) Airway Management 6 0.83-1.00 0.94 4 (66.7) Mechanical Ventilation 10 0.83-1.00 0.93 7 (70.0) Emergency & Critical Care 8 0.83-1.00 0.92 5 (62.5) Communication 5 0.83-1.00 0.93 3 (60.0) Leadership 5 0.83-1.00 0.89 3 (50.0) Ethics & Professionalism 4 0.83-1.00 0.90 3 (60.0) Evidence-Based Practice 6 0.67-1.00 0.86 3 (50.0) Neonatal/Pediatric Care 4 0.67-1.00 0.86 3 (50.0) Pulmonary Rehabilitation 4 0.67-1.00 0.87 3 (60.0) Sleep Medicine 4 0.67–0.83 0.77 0 (0.0) Home Care 4 0.83-1.00 0.91 4 (66.7) Overall (83 items) 83 0.33-1.00 0.90 54 (65.1) Phase 2: Participant Characteristics Of the 80 invited respiratory therapists, 58 (72.5%) completed the RT-LNI, and 45 of these (77.6%) also completed the evaluation survey (Fig. 1 ). Most were employed at Rashid Hospital (48, 82.8%), with 5 (8.6%) at Al Jalila Children's Hospital and 5 (8.6%) at Latifa Hospital. Thirty-five participants (60.3%) reported 1–10 years of experience, 16 (27.6%) had 11–20 years, and 7 (12.1%) had more than 20 years. Thirty-two (55.2%) held a bachelor’s degree, while 26 (44.8%) had postgraduate qualifications. The majority worked in critical care/ICU settings (42, 72.4%), followed by acute care (14, 24.1%), emergency departments (8, 13.8%), and neonatal/pediatric specialties (4, 6.9%). Forty-one participants (70.7%) reported prior exposure to learning needs Instruments. Participant characteristics are summarized in Table 2 . Table 2 Participant Characteristics (n = 58) Characteristic n (%) Hospital Rashid Hospital 48 (82.8) Al Jalila Children’s Hospital 5 (8.6) Latifa Hospital 5 (8.6) Years of Experience 1–10 years 35 (60.3) 11–20 years 16 (27.6) >20 years 7 (12.1) Education Bachelor’s degree 32 (55.2) Postgraduate 26 (44.8) Practice Setting† Critical Care/ICU 42 (72.4) Acute Care 14 (24.1) Emergency Department 8 (13.8) Neonatal/Pediatric 4 (6.9) Rehabilitation/Long-term Care 3 (5.2) Prior LNA Tool Use Yes 41 (70.7) No 17 (29.3) †Participants could select more than one practice setting; percentages sum to > 100%. Reliability and Internal Consistency Cronbach's alpha coefficients across the 13 domains ranged from 0.70 to 0.95, with a median of 0.91. Results are detailed in Table 3 . Domains with excellent internal consistency (alpha 0.90 or above) included Respiratory Assessment and Diagnostics (0.94, 95% CI 0.91–0.96), Oxygen and Aerosol Therapy (0.92, 95% CI 0.88–0.95), Airway Management (0.95, 95% CI 0.93–0.97), Mechanical Ventilation (0.93, 95% CI 0.90–0.95), Emergency and Critical Care (0.94, 95% CI 0.91–0.96), Evidence-Based Practice and Research (0.92, 95% CI 0.88–0.95), and Sleep Medicine (0.91, 95% CI 0.86–0.94). Domains with good consistency (0.80–0.89) included Communication (0.84, 95% CI 0.78–0.89), Leadership (0.82, 95% CI 0.75–0.87), and Pulmonary Rehabilitation (0.80, 95% CI 0.72–0.86). Ethics (0.70, 95% CI 0.58–0.80), Professional Development (0.71, 95% CI 0.59–0.80), and Home Care (0.73, 95% CI 0.62–0.82) meet the acceptable threshold. Corrected item-total correlations within each domain ranged from 0.38 to 0.89 (one item in the Ethics domain yielded 0.31 before domain-level factor extraction; see Additional file 3 for complete matrices). Within the Airway Management domain (alpha = 0.95), three item pairs showed inter-item correlations exceeding 0.80, indicating potential redundancy that should be addressed in future instrument refinement to decrease respondent burden. Construct Validity: Domain-Level Exploratory Factor Analysis Given the pilot sample size (n = 58), exploratory factor analysis was performed within each domain separately to ensure adequate subject-to-variable ratios (ranging from 4.1:1 to 14.5:1). This method assesses the unidimensionality of each domain rather than the entire factorial structure of the instrument. These findings should therefore be viewed as preliminary; a larger sample and confirmatory factor analysis are necessary to determine the full factor structure. Principal axis factoring with varimax rotation was performed on all 13 domains. KMO values ranged from 0.64 (Home Care) to 0.88 (Mechanical Ventilation), indicating adequate to excellent sampling adequacy (Table 3 ). Bartlett's test of sphericity was statistically significant (p less than 0.001) for all domains, confirming sufficient inter-item correlations to support factor analysis. For each domain, a single dominant factor was extracted based on parallel analysis supplemented by the Kaiser criterion (eigenvalue greater than 1.0), with one exception: the Emergency and Critical Care domain yielded two factors, the first explaining 72.4% of the variance and the second accounting for an additional 9.1%. In the two-factor solution, Factor 1 was characterized by acute resuscitation items (CPR, rapid response, trauma care, ACLS; loadings 0.78–0.94), while Factor 2 was defined by prolonged critical care management items (hemodynamic monitoring, transport, sepsis recognition; loadings 0.61–0.72). Loadings on the primary factor ranged from 0.52 to 0.96 across all domains (median 0.79). Three items across the instrument had primary factor loadings between 0.40 and 0.52; no items fell below 0.40. Corrected item-total correlations ranged from 0.38 to 0.89. The cumulative variance explained by the extracted factors ranged from 67.8% to 91.0%. A sensitivity analysis using oblique (promax) rotation produced consistent factor structures across all 13 domains. Complete factor loading matrices, communalities, and corrected item-total correlations for all 13 domains are provided in Additional file 3. Table 3 Reliability and Construct Validity by Domain Domain Items α (95% CI) KMO Var % Loading Range S:V Resp. Assessment 8 0.94 (0.91–0.96) 0.85 82.3 0.68–0.93 7.3:1 Oxygen & Aerosol 7 0.92 (0.88–0.95) 0.82 78.6 0.71–0.91 8.3:1 Airway Mgmt 6 0.95 (0.93–0.97) 0.86 91.0 0.82–0.96 9.7:1 Mech. Vent. 10 0.93 (0.90–0.95) 0.88 76.4 0.62–0.90 5.8:1 Emergency & CC 8 0.94 (0.91–0.96) 0.84 81.5 0.65–0.94 7.3:1 Communication 5 0.84 (0.78–0.89) 0.74 70.1 0.59–0.85 11.6:1 Leadership & PD 5 0.82 (0.75–0.87) 0.72 68.4 0.55–0.83 11.6:1 Ethics 4 0.70 (0.58–0.80) 0.66 67.8 0.52–0.79 14.5:1 EBP & Research 6 0.92 (0.88–0.95) 0.83 80.7 0.70–0.93 9.7:1 Neo/Peds 4 0.89 (0.84–0.93) 0.79 77.8 0.64–0.90 14.5:1 Pulm Rehab 4 0.80 (0.72–0.86) 0.71 71.3 0.56–0.84 14.5:1 Sleep Medicine 4 0.91 (0.86–0.94) 0.80 84.1 0.74–0.92 14.5:1 Home Care 4 0.73 (0.62–0.82) 0.64 74.6 0.61–0.87 14.5:1 Discriminant Validity Inter-domain Spearman correlations ranged from 0.22 to 0.78, with a median of 0.51. The highest correlation was found between Mechanical Ventilation and Emergency and Critical Care (rho = 0.78), which is expected due to the clinical overlap between these domains. No inter-domain correlation exceeded 0.85, supporting the distinctiveness of the 13 domain constructs [ 59 ]. Learning Needs Patterns Participants reported the highest self-assessed competence in core technical skills relevant to tertiary care practice: Airway Management (mean 3.9, SD 0.6), Mechanical Ventilation (mean 3.8, SD 0.7), Oxygen and Aerosol Therapy (mean 3.8, SD 0.5), and Emergency and Critical Care (mean 3.7, SD 0.6). Non-technical skills were rated at moderate levels: Communication (mean 3.3, SD 0.8), Ethics and Professionalism (mean 3.2, SD 0.7), Professional Development (mean 3.1, SD 0.8), and Leadership (mean 3.0, SD 0.9). Domain-level competency scores are shown in Fig. 2 . The lowest self-assessed competence, indicating priority learning needs, was seen in Evidence-Based Practice and Research (mean 2.4, SD 1.1), Sleep Medicine (mean 2.6, SD 1.2), Pulmonary Rehabilitation (mean 2.7, SD 1.0), and Neonatal/Pediatric Care (mean 2.8, SD 1.1). These areas are less often encountered in the mostly adult critical care settings where most participants worked. Analysis of open-ended responses (n = 42 respondents providing at least one response) revealed recurring themes. Research and evidence-based practice, specifically critical appraisal skills and understanding of research design, were identified as a priority by 23 respondents (54.8%). Sleep medicine training was mentioned by 15 respondents (35.7%), and pulmonary rehabilitation by 11 (26.2%). Non-technical skill development, particularly conflict resolution and leadership in resource-constrained environments, was noted by 22 respondents (52.4%). Subgroup comparisons using Mann-Whitney U tests with Bonferroni correction (adjusted alpha = 0.002) showed no statistically significant differences in competency ratings between experience groups (1–10 years vs. 11 or more years) or between those with and without prior LNA tool use (Table 4 ). The largest unadjusted effect was observed for Evidence-Based Practice by experience group (unadjusted p = 0.06, rank-biserial r = 0.18), where more experienced RTs rated slightly higher, but this did not remain significant after correcting for multiple comparisons. All effect sizes were negligible to small (rank-biserial r = 0.02–0.21), well below the threshold for medium effects (r = 0.30). Post-hoc power analysis confirmed that with n = 58 and alpha = 0.002, only large effects (r ≥ 0.52) could be detected with 80% power, indicating that the analysis was underpowered to find small-to-medium subgroup differences. These results are therefore consistent with either a true similarity in self-assessed learning needs across subgroups or insufficient power to detect existing differences; a definitive subgroup analysis will require sample sizes of at least 100 per group. Table 4 Selected Subgroup Comparisons by Experience Group Domain Mdn [IQR] 1–10 yrs Mdn [IQR] 11 + yrs U p (unadj.) p (Bonf.) r EBP & Research 2.2 [1.5-3.0] 2.7 [1.8–3.3] 321 0.06 1.00 0.18 Sleep Medicine 2.5 [1.8–3.3] 2.8 [2.0-3.5] 345 0.14 1.00 0.14 Mech. Vent. 3.8 [3.3–4.2] 3.9 [3.5–4.3] 368 0.38 1.00 0.08 Airway Mgmt 3.9 [3.5–4.3] 4.0 [3.6–4.4] 385 0.58 1.00 0.05 4 of 13 domains shown, selected to illustrate the range from largest to smallest observed effects. User Evaluation and Acceptability User evaluation surveys were completed by 45 of 58 participants (77.6%). Results showed strong acceptability across all dimensions (Table 5 ). Clarity and usability (6 items) had a combined mean of 25.4/30 (84.8%, with a mean of 4.23/5 per item). Content relevance (5 items) had an average of 4.30/5 per item. Perceived value and effectiveness (5 items) scored 4.28/5. Reasibility (4 items) received a mean of 4.31/5. Overall satisfaction (3 items) rated 4.20/5. The overall mean across all evaluation items was 4.26/5 (85.3% of the maximum score). Respondents estimated the completion time at 15–20 minutes and showed willingness to use the tool annually. Open comments expressed appreciation for the tool's thorough coverage of both technical and non-technical skills, the inclusion of emerging competencies like lung ultrasound, and the value of structured self-reflection for professional growth planning. Table 5 User Evaluation Results (n = 45) Dimension Items Cum. Mean (SD) Mean/Item % Max Clarity & Usability 6 25.4 (4.3) 4.23 84.7 Relevance of Content 5 21.5 (3.2) 4.30 86.0 Perceived Value 5 21.4 (3.5) 4.28 85.6 Feasibility 4 17.2 (2.8) 4.31 86.2 Overall Satisfaction 3 12.6 (2.1) 4.20 84.0 Overall 23 98.1 (14.2) 4.26 85.3 DISCUSSION Summary of Key Findings This study introduces the RT-LNI, an instrument built around competencies, specifically made for respiratory therapists. According to a thorough search of PubMed, CINAHL, and Scopus, it appears to be the first evidence-based, profession-specific assessment of learning needs for respiratory therapy. The RT-LNI shows strong content validity, good to excellent internal consistency, initial support for construct validity at the domain level, and high acceptance among users. In addition to its psychometric strengths, the tool reveals meaningful patterns of self-rated competence that directly impact the quality of respiratory care and guide professional development. Content Validity The S-CVI/Ave of 0.90 (95% CI 0.86–0.94) surpasses the recommended thresholds by Lynn [ 16 ] and Polit and Beck [ 17 ] for excellent content validity. This result is comparable to similar validation studies of instruments in allied health fields; for instance, Gaspard and Yang [ 7 ] found an S-CVI/Ave of 0.87 for a training needs Instrument in Saint Lucia, while Bartholomew et al. [ 8 ] reported an S-CVI/Ave of 0.89 for an allied health workforce needs assessment in Australia. The universal agreement index (S-CVI/UA = 0.65) aligns with studies using panels of six or more experts, where achieving perfect consensus is inherently more challenging [ 18 ]. The lower CVI scores for sleep medicine items highlight an important methodological issue: the composition of the expert panel can systematically affect content validation results. The mostly critical care-focused panel may have underestimated skills outside their routine practice. Future content validation efforts should include experts from all relevant areas being assessed. Psychometric Properties Internal consistency, indicated by Cronbach's alpha values ranging from 0.70 to 0.95, was acceptable to excellent across all domains. However, the very high alpha coefficients in some technical areas, such as Airway Management (alpha = 0.95), should be interpreted with caution. While high reliability is beneficial, alpha values above 0.95 may suggest item redundancy, a narrow definition of the construct, or ceiling effects within those domains [ 59 ]. Notably, the top-scoring domains, Airway Management (mean 3.9/5) and Mechanical Ventilation (mean 3.8/5), exhibited compressed score distributions at the upper end of the Dreyfus scale, indicating potential ceiling effects that might artificially inflate internal consistency by limiting response variability. Testing in settings with a broader range of skills could better distinguish higher levels of competency. Within the Airway Management domain, three item pairs had correlations (r) greater than 0.80: artificial airway insertion, endotracheal intubation assistance, and tracheostomy care. These items address distinct procedures, oral/nasal intubation, surgical airway management, and tracheostomy maintenance, and were kept because they represent separate competency components per the AARC and CSRT frameworks. Future revisions should assess whether responses can differentiate among these items, or whether combining them into a single "advanced airway procedures" item might reduce respondent burden without losing essential content. Domain-level exploratory factor analysis provided initial evidence of construct validity. Methodological research shows that the adequacy of EFA depends less on fixed sample size thresholds and more on the quality of the factor model, such as strong communalities, clear loadings, and adequate indicators per factor, that can support interpretable results even with relatively small samples [ 32 , 33 , 34 ]. In this study, each domain mostly exhibited a unidimensional structure, with strong factor loadings (median 0.79, range 0.52–0.96) and high communalities, confirming the internal consistency of the competency domains under favorable structural conditions. The only exception was the Emergency and Critical Care domain, which revealed a two-factor solution: Factor 1 (72.4% of variance) encompassed competencies related to acute resuscitation and emergency stabilization (CPR, rapid response, trauma, ACLS), while Factor 2 (additional 9.1% variance) included prolonged critical care management skills (hemodynamic monitoring, transport, sepsis management). This clinically meaningful split suggests that the Emergency and Critical Care domain may comprise two related but distinct sub-constructs, a finding that should inform future domain refinement either through formal splitting or item revision to enhance unidimensionality. A sensitivity analysis using oblique (promax) rotation yielded factor structures consistent with the varimax solution across all 13 domains, with factor correlations within rotated solutions remaining below 0.40, supporting the appropriateness of orthogonal rotation for this instrument. KMO values ranged from 0.64 (Home Care, Ethics) to 0.88 (Mechanical Ventilation); the two domains with marginal KMO values (below the conventional 0.70 threshold) each contained only four items, and their factor solutions showed strong loadings and sufficient variance explained, aligning with evidence that KMO thresholds are less strict when there are few items per factor and loadings are strong [ 34 ]. These marginal KMO values should be interpreted with caution given the small sample; KMO itself is sensitive to sample size, and values in the 0.60–0.70 range at n = 58 do not preclude interpretable factor solutions but do suggest that replication with larger samples is necessary to confirm stability. Nevertheless, these exploratory results should be interpreted cautiously. Confirmatory factor analysis with a larger independent sample (n greater than or equal to 300) is crucial to test the measurement model and analyze cross-domain item complexity that within-domain analyses cannot identify [ 35 , 36 , 37 ]. Inter-domain correlations (Spearman's rho 0.22–0.78) support the discriminant validity of the 13-domain structure. The highest observed correlation (Mechanical Ventilation and Emergency Critical Care, rho = 0.78) is expected clinically, given the overlap in competencies required for these closely related practice areas. No correlation exceeded the 0.85 threshold that would indicate domain redundancy [ 59 ]. Clinical Implications of Learning Needs Findings The identified learning needs patterns from the RT-LNI have significant implications for improving respiratory care quality and workforce growth. When viewed through the Dreyfus model underlying the RT-LNI response scale, a mean score of 2.4 out of 5 for evidence-based practice (EBP) aligns with the "Advanced Beginner" stage, suggesting that RTs see themselves as capable of performing EBP tasks with regular supervision but not independently. Conversely, core technical skills scored between 3.7 and 3.9 out of 5, corresponding to the "Proficient" stage, indicating independent practice with situational adjustments. The approximately 1.5-stage gap between EBP and core technical skills on the Dreyfus scale (Fig. 2 ) highlights a clinically important disparity that can directly impact the quality of respiratory care provided. The EBP finding aligns with international surveys of respiratory therapists. Zaccagnini et al. [ 4 ] identified research knowledge and evidence-based practice as priority gaps among Canadian RTs, and Clark et al. [ 1 ] reported limited self-efficacy in evidence-based practice skills among US respiratory therapists. The recent development of a Scholarly Practice Tool for Canadian RTs [ 51 ] further emphasizes the profession's recognition that scholarly and EBP competencies need dedicated assessment and development. These consistent findings across various healthcare systems point to a systemic gap in RT education and CPD programs, which may impact the adoption of evidence-based respiratory care, patient outcomes, and the profession's ability to engage in practice-based research [ 13 ]. Similarly, the identified learning needs in sleep medicine (mean 2.6/5) and pulmonary rehabilitation (mean 2.7/5), both at the upper "Advanced Beginner" level, are directly relevant to the quality of respiratory care services. These scores probably reflect limited clinical exposure rather than a lack of ability; 72.4% of participants mainly worked in ICU settings where sleep medicine and rehabilitation are rarely encountered. These specialties are growing areas of RT practice that may be underserved by current educational programs [ 10 ]. Systematic identification of these gaps using validated tools like the RT-LNI can help institutions make evidence-based decisions about CPD investment priorities, potentially enhancing the quality and scope of respiratory care services. The moderate ratings in non-technical competencies, especially leadership (average 3.0/5, labeled as "Competent") and professional development (average 3.1/5), align with emerging research on the importance of non-technical skills in interprofessional team function and patient safety [ 13 , 23 ]. The open-ended response data, where 52.4% of respondents identified non-technical skill development as a priority, offers supporting evidence that the RT-LNI reflects meaningful perceptions of learning needs that practitioners themselves recognize as important. Using Kern's six-step educational design framework [ 15 ], these findings directly inform Step 2 (targeted needs assessment): a CPD program aimed at closing the EBP gap might focus on advancing from "Advanced Beginner" to "Competent" through structured evidence synthesis workshops, journal club participation, and mentored quality improvement projects. The RT-LNI would serve both as the initial assessment and as the measure of CPD effectiveness. Self-Assessment Validity Considerations The RT-LNI depends on self-assessment of competence, and interpreting its results must consider extensive evidence on the accuracy of self-assessment in health professions. Systematic reviews and meta-analyses consistently show weak to moderate alignment between self-assessed and objectively measured competence [ 27 , 28 , 40 ]. This miscalibration follows a well-known pattern: lower performers tend to overestimate their abilities, while higher performers may underestimate [ 28 , 42 ], a trend confirmed in recent studies involving medical students [ 40 ], surgical residents [ 41 ], and endoscopy practitioners [ 42 ]. Notably, self-assessment accuracy varies by domain. Procedural skills tend to be better calibrated than knowledge-based or non-technical skills [ 42 , 43 ], and by experience level, with more experienced clinicians showing closer agreement with external ratings [ 42 ]. For the RT-LNI, this indicates that the learning gaps identified in areas like EBP (average 2.4/5) may underestimate actual needs, especially among less experienced RTs who might lack the metacognitive awareness to recognize their own limitations. However, the development of self-assessment research since the mid-2000s offers important context for understanding these findings. The field has shifted from questioning whether practitioners can accurately self-assess in theory to creating systems that support informed self-assessment and self-judgment that incorporate external feedback, explicit standards, observation, and reflection [ 47 , 48 , 49 ]. Sargeant et al. [ 47 ] argued that self-assessment becomes educationally valuable when it is integrated into such systems rather than viewed as an isolated personal skill. Growing evidence indicates that structured self-assessment with clear standards and feedback can help identify learning needs and promote self-regulated improvement, even though retrospective global self-ratings are still poor tools for summative judgment [ 50 ]. This distinction is critical: the RT-LNI is designed to identify formative learning needs, not to certify summative competency. Its alignment with AARC and CSRT/NCF competency frameworks provides the normative benchmark that turns isolated self-rating into informed self-assessment. Furthermore, the non-significant differences between the experience groups (1–10 vs. 11 + years) should be interpreted in light of the existing literature. Although the small sample size (n = 58) restricts the ability to detect subgroup differences, the small effect sizes observed (r = 0.02–0.18) align with two possible explanations: either there is genuine similarity in perceived learning needs across experience levels, or there is differential miscalibration, where less experienced RTs overestimate their competence while more experienced RTs underestimate, resulting in artificially similar group averages. Clarifying these possibilities requires future research that compares RT-LNI self-assessments with objective competency measures such as simulation assessments, clinical audits, multi-source feedback, or knowledge testing [ 44 , 45 , 52 ]. Limitations Several limitations should be acknowledged. First, this single-site study conducted within Dubai Health restricts the generalizability of the findings, as the scope of RT practice varies between countries. Second, although the pilot sample size (n = 58) is adequate for initial psychometric evaluation, it is too small for confirmatory factor analysis or detailed subgroup comparisons; thus, EFA results need replication. Third, the cross-sectional design prevents assessment of test-retest reliability. Fourth, self-assessment may be affected by systematic biases that impact accuracy. Fifth, the primarily critical care expert panel might have introduced specialty bias into CVI ratings. Sixth, the open-access survey link could lead to duplicate responses, although this risk is reduced by anonymous, voluntary participation. Seventh, the optional user evaluation (77.6% response rate) may introduce non-response bias, potentially inflating satisfaction ratings. Eighth, participant demographics did not include gender or nationality, which limits the ability to analyze differential item functioning in this diverse workforce; future studies should gather these variables. Finally, administering the survey in English only might influence interpretation among Dubai's multicultural RT community, where practitioners from South Asian, Arab, and African expatriate backgrounds may interpret Dreyfus-anchored scale items differently across cultural contexts. Developing an Arabic translation and cultural adaptation is the next essential step. Future Directions Future research should focus on three key areas. First, conducting multisite validation with diverse RT populations across various healthcare systems and regions is crucial, allowing confirmatory factor analysis with sufficient sample sizes (n > 300) and testing measurement invariance across settings. Additionally, establishing test-retest reliability is important to evaluate score stability over clinically relevant timeframes. Second, responsiveness, the instrument's ability to detect meaningful change following CPD interventions, is a crucial next step for establishing the RT-LNI's usefulness within Kern's educational design framework. Existing evidence supports the feasibility of this approach: validated CPD self-report tools, such as the CPD-REACTION [ 53 ] and the SE-12 [ 54 ], have demonstrated sensitivity to educational change in pre-post studies. Responsiveness assessment for the RT-LNI should use a pre-post design focused on specific CPD activities (e.g., EBP workshops for RTs at the "Advanced Beginner" level), with effect sizes, reliable change indices, and follow-up behavioral data to determine whether score changes indicate genuine learning. Establishing a minimal clinically important difference (MCID) on the Dreyfus scale, for example, whether a one-point shift from "Advanced Beginner" to "Competent" signifies meaningful progress, would enhance interpretability. Current measurement guidance views responsiveness as longitudinal validity demonstrated through hypothesis-driven change analyses rather than requiring a pre-established MCID [ 55 , 56 ]. Third, convergent validity should be assessed by comparing RT-LNI self-assessments with objective competency measures such as simulation-based assessments, clinical audit data, multi-source feedback from supervisors and peers, and knowledge testing [ 44 , 52 ]. This would clarify the relationship between self-perceived and objectively measured learning needs and help calibrate the self-assessment results. Developing Arabic-language versions and specialty-specific modules (e.g., neonatal/pediatric, sleep medicine) would increase applicability across the UAE's diverse RT workforce and beyond. CONCLUSIONS The Respiratory Therapy Learning Needs Instrument (RT-LNI) offers the first published, preliminary validation evidence for a competency-based self-Instrument specifically created for respiratory therapists. The tool shows strong content validity (S-CVI/Ave = 0.90), acceptable to excellent internal consistency across all 13 competency domains (Cronbach's alpha 0.70–0.95), initial construct validity evidence at the domain level, and high user acceptability (overall mean 4.26/5). The RT-LNI identified significant patterns of learning needs, highlighting evidence-based practice, sleep medicine, and pulmonary rehabilitation as key areas for targeted ongoing professional development. These findings are directly relevant to improving the quality of respiratory care services and planning workforce development. While these initial findings are promising, multi-site validation with larger and more diverse samples is necessary before recommending the RT-LNI for routine clinical use. Future research should include confirmatory factor analysis, test-retest reliability evaluations, responsiveness studies, and integration with objective competency measures to strengthen the validity evidence base. Declarations Ethics Approval and Consent to Participate This study was approved by the Mohammed Bin Rashid University of Medicine and Health Sciences Institutional Review Board (IRB reference 202400216, approval date March 2025). Site-specific approvals were obtained from Rashid Hospital, Al Jalila Children's Hospital, and Latifa Hospital. Electronic informed consent was obtained from all participants. Consent for Publication Not applicable. Availability of Data and Materials The RT-LNI instrument is provided in Additional file 4. The datasets analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This research received no specific grant from any funding agency, commercial entity, or not-for-profit organization. Author Contributions MDD conceptualized and designed the study, developed the RT-LNI instrument, collected and analyzed data, and drafted the manuscript. LP contributed to study design, instrument refinement, and critical revision of the manuscript. AHK contributed to the study design and analysis, and critical revision of the manuscript. 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Supplementary Files ADDITIONALFILESlegends.docx Additionalfile1STROBEChecklist.docx Additional file 1: STROBE checklist for cross-sectional studies Additionalfile2CompetencyMapping.docx Additional file 2: Mapping of RT-LNI items to AARC and CSRT/NCF competency frameworks Additionalfile3FactorLoadings.docx Additional file 3: Complete factor loading matrices for all 13 competency domains Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9484867","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":627945847,"identity":"92f22813-7fcc-4269-93c1-4d069ffca9e4","order_by":0,"name":"Mishal Delma Dsouza","email":"","orcid":"","institution":"Rashid Hospital, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Mishal","middleName":"Delma","lastName":"Dsouza","suffix":""},{"id":627945848,"identity":"0904c9a9-f934-44ac-a519-caa857bc495d","order_by":1,"name":"Leigh Powell","email":"","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Leigh","middleName":"","lastName":"Powell","suffix":""},{"id":627945849,"identity":"d948d2f0-5f9c-430e-8317-e92dd6019047","order_by":2,"name":"Amar Hassan Khamis","email":"","orcid":"","institution":"Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health","correspondingAuthor":false,"prefix":"","firstName":"Amar","middleName":"Hassan","lastName":"Khamis","suffix":""},{"id":627945850,"identity":"59e27199-b90d-4889-94e1-738fe9d49a2e","order_by":3,"name":"Nabil Zary","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIie3PsQrCMBCA4SuFdgnUsVLQV0gXRUR8lRQHlyq6OYmTLj5ABt9CcHKIZHCpuAa6OHUS6SIIOhjTPWYUzA+FG/JxPQCb7SdDwC5qcJlrTogaPGJOoCIIm5G2fzrILfNmm6b37XTPIVgxpyw1pLMeE0m8eCNGu5wWHMKMuHWqIZilWBLk0FASxDiAAIiQjpyvHxL2aZgWijQFuM+Xjgi1BSeSeIpgAV6kOx+Lm7wFkwFFRSunbIjiLFl21tofG/GynM171B8U+YR1G40j5+KhW1NB+dWIGuXhzuIrqAqY4UObzWb7u97wBVEG8Apy4AAAAABJRU5ErkJggg==","orcid":"","institution":"Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai Health","correspondingAuthor":true,"prefix":"","firstName":"Nabil","middleName":"","lastName":"Zary","suffix":""}],"badges":[],"createdAt":"2026-04-21 13:23:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9484867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9484867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107621547,"identity":"9ecd29b9-6ed4-4fe6-9b3c-17d99653db1e","added_by":"auto","created_at":"2026-04-23 09:41:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":409690,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flow diagram for RT-LNI development and pilot testing. Phase 1 (left) shows content validation by six expert respiratory therapists, resulting in a reduction from 83 to 75 items across 13 competency domains. Phase 2 (right) illustrates pilot testing with 58 licensed respiratory therapists across three Dubai Health tertiary care facilities. Blue boxes represent process steps, yellow indicates decision/revision points, green shows outcomes, and purple marks analysis outputs. Abbreviations: CVI, content validity index; EFA, exploratory factor analysis; RT-LNI, Respiratory Therapy Learning Needs Instrument.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9484867/v1/1f29432b2cbc7047193738a4.png"},{"id":107621568,"identity":"98741df8-47bd-4ae7-843b-99b8e017092d","added_by":"auto","created_at":"2026-04-23 09:42:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":357949,"visible":true,"origin":"","legend":"\u003cp\u003eSelf-assessed competence across 13 RT-LNI domains (n = 58). Horizontal bars show mean scores on the Dreyfus scale (1 = Novice to 5 = Expert); error bars display standard deviations. Bar colors indicate Dreyfus stages: green = Proficient (3.5–4.5), orange = Competent to Advanced Beginner (2.5–3.5), red = lower Advanced Beginner (≤2.5). Dashed vertical lines mark Dreyfus stage boundaries. Domains are grouped by RT-LNI sections: Technical Skills (5 domains), Non-Technical Skills (4 domains), and Specialized Practice (5 domains, including the lowest-scoring domain, Evidence-Based Practice at 2.4/5). The bracket illustrates the approximately 1.5 Dreyfus-stage gap between the highest-scoring domain (Airway Management, 3.9) and the lowest-scoring domain (Evidence-Based Practice, 2.4). Abbreviations: EBP, evidence-based practice; RT-LNI, Respiratory Therapy Learning Needs Instrument.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9484867/v1/d434927b1ae5848bd771b834.png"},{"id":107707353,"identity":"3faad7a9-82d7-440e-a420-7cf977cc771f","added_by":"auto","created_at":"2026-04-24 09:20:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1192042,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9484867/v1/25cd3519-a681-491d-9399-b7185b39fcd6.pdf"},{"id":107621559,"identity":"cb7625ed-63e3-4977-83fd-e6c01e893508","added_by":"auto","created_at":"2026-04-23 09:42:07","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":23208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"ADDITIONALFILESlegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-9484867/v1/beabb72a0a9d0eb8bc7be494.docx"},{"id":107621565,"identity":"188750ab-95f3-49ad-9aa6-3d43f183869b","added_by":"auto","created_at":"2026-04-23 09:42:08","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29637,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: STROBE checklist for cross-sectional studies\u003c/p\u003e","description":"","filename":"Additionalfile1STROBEChecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-9484867/v1/d598612919ad69580d881415.docx"},{"id":107621555,"identity":"783bee7c-b12f-4dcc-8025-f8568ab3ad5f","added_by":"auto","created_at":"2026-04-23 09:42:05","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":35743,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Mapping of RT-LNI items to AARC and CSRT/NCF competency frameworks\u003c/p\u003e","description":"","filename":"Additionalfile2CompetencyMapping.docx","url":"https://assets-eu.researchsquare.com/files/rs-9484867/v1/74917858ee24b003b5a8ecc4.docx"},{"id":107621560,"identity":"c303ad40-336a-4594-a02b-194c005f3d3e","added_by":"auto","created_at":"2026-04-23 09:42:07","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":38004,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 3: Complete factor loading matrices for all 13 competency domains\u003c/p\u003e","description":"","filename":"Additionalfile3FactorLoadings.docx","url":"https://assets-eu.researchsquare.com/files/rs-9484867/v1/c3a904f0955b8a7b25b54a19.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Initial Validation of the Respiratory Therapy Learning Needs Instrument (RT-LNI): A Competency-Based Self-Assessment Instrument","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eRespiratory therapy is a rapidly advancing healthcare field that is essential in managing both acute and chronic cardiopulmonary conditions in various clinical environments [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Since its inception, the profession has grown considerably, shifting from a primary emphasis on oxygen delivery to include sophisticated therapeutic techniques such as mechanical ventilation, non-invasive respiratory support, point-of-care diagnostics, and emergency critical care procedures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Today, respiratory therapy is a well-established global profession, with recognized practitioners in North America, Europe, the Middle East, and the Asia-Pacific region [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Additionally, organizations like the American Association for Respiratory Care (AARC) and the Canadian Society of Respiratory Therapists (CSRT) have developed comprehensive competency frameworks to standardize practice [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, the profession encounters significant challenges in keeping practitioners' skills current amid rapid technological progress and rising patient complexity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Respiratory therapy practice is continually broadening, with RTs taking on roles in high-flow nasal cannula therapy, point-of-care ultrasound, ventilator liberation protocols, and interprofessional leadership [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This expansion necessitates the systematic identification and fulfillment of substantial learning needs through ongoing professional development (CPD) efforts [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough learning needs assessment is crucial for effective CPD, a systematic review of PubMed, CINAHL, and Scopus conducted in January 2026 using terms like \"respiratory therap*\" AND \"learning needs\" OR \"needs assessment\" OR \"competency assessment\" AND \"instrument\" OR \"tool\" OR \"validation\" revealed no published, psychometrically validated, profession-specific learning needs Instruments for respiratory therapists. Current approaches mostly use generic surveys adapted from nursing and allied health fields or rely on cross-sectional snapshots not connected to validated competency frameworks [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A scoping review by Al-Ismail et al. found that most CPD learning needs Instruments across health professions are general, with none specifically designed and validated for respiratory therapy.\u003c/p\u003e \u003cp\u003eThis lack of validated instrumentation has notable practical implications. Without a specialized, competency-focused Instrument tailored to the profession, institutions struggle to systematically evaluate learning needs across various practice areas, make informed decisions about continuing professional development (CPD), or monitor workforce progress over time [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Clinically, unrecognized learning needs can lead to less effective respiratory care, slower implementation of evidence-based practices, and missed chances for quality enhancement [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe RT-LNI's development was guided by three complementary theoretical frameworks. Firstly, Kern's six-step model for curriculum development in medical education [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] offered the overall structure, emphasizing needs assessment as the initial step for creating targeted educational interventions. Secondly, Bradshaw's taxonomy of needs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] influenced the design of the instrument by differentiating between normative needs (defined by expert standards), felt needs (perceived by practitioners), expressed needs (specific demands for training), and comparative needs (gaps compared to peers). The RT-LNI mainly measures felt needs through self-assessment, while its alignment with AARC and CSRT competency frameworks provides the normative benchmarks for interpreting self-perceived competence. Thirdly, the Dreyfus model of skill acquisition [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] shaped the five-level rating scale (Novice to Expert), offering practitioners clear behavioral anchors for self-evaluation.\u003c/p\u003e \u003cp\u003eChoosing self-assessment as the main measurement method requires clear justification. In health professions, literature shows only weak to moderate correlation between self-assessed and objectively measured competence [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Systematic reviews reveal a pattern: lower performers often overestimate their skills, while higher performers may underestimate, a trend seen among medical students [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], residents [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and clinicians [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This miscalibration varies by domain; procedural skills tend to be more accurately assessed than knowledge-based or non-technical areas [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and it also depends on feedback availability and external benchmarks [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. A recent scoping review indicates that despite these limitations, self-assessment remains widely used as an outcome measure in health education, emphasizing the importance of defining its purpose carefully [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe conceptual shift led by Eva, Regehr, and Sargeant [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] redefines self-assessment not as an isolated measure of competence accuracy but as a socially and contextually based process of self-judgment. This process becomes educationally valuable when it is guided by external standards, feedback, and reflection. Instead of questioning whether practitioners can precisely evaluate their competence in an abstract sense, the key issue is whether structured self-assessment within competency frameworks can assist in identifying learning needs and supporting self-regulated professional growth [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The RT-LNI embraces this approach of informed self-assessment: it does not aim to measure objective competence but to recognize perceived learning needs within the normative structure of established competency frameworks (AARC, CSRT/NCF), offering both self-assessment and external benchmarks to contextualize responses. Self-assessment remains the most practical method for large-scale, routine identification of learning needs in clinical environments, and it is well recognized as useful for guiding personalized CPD planning when properly framed [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe purpose of this study was to develop and perform an initial validation of the RT-LNI, a comprehensive, competency-based self-Instrument specifically designed for respiratory therapists. The instrument covers technical skills, non-technical competencies, and specialized practice areas based on the AARC and CSRT/National Competency Framework (NCF). This study presents the content validation, initial psychometric assessment, learning needs analysis, and user acceptability results from a pilot study conducted within Dubai Health, an integrated public academic health system.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis study used a quantitative, descriptive, cross-sectional approach in two phases: initially, content validation and instrument refinement, followed by pilot testing, psychometric assessment, and evaluation of user acceptability. The research received approval from the Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU) Institutional Review Board (IRB reference 202400216). The reporting adhered to the STROBE checklist for cross-sectional studies (see Additional file 1) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eData collection took place at three Dubai Health government tertiary care facilities: Rashid Hospital, Al Jalila Children's Hospital, and Latifa Hospital. Dubai Health is the first integrated academic health system in Dubai, comprising multiple hospitals, primary care centers, and academic institutions throughout the Emirate. After IRB approval in March 2025, the period from April to August 2025 was dedicated to recruiting expert panels, validating content, revising instruments, obtaining site-specific approvals, and setting up the electronic survey. Pilot data collection was conducted between September and October 2025. A participant flow diagram is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhase 1: Tool Development and Content Validation\u003c/h3\u003e\n\u003cp\u003eItem generation for the RT-LNI was based on three sources: (1) the AARC Entry to Respiratory Therapy Practice 2030 competency framework [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], (2) the CSRT National Competency Framework (NCF) for Respiratory Therapists [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and (3) peer-reviewed literature on RT skills and learning needs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A mapping table showing how each RT-LNI item aligns with its source competency framework is available in Additional file 2.\u003c/p\u003e \u003cp\u003eThis process generated 83 candidate items, divided into 3 sections and 13 competency domains: Section 1: Technical Skills and Knowledge (including Respiratory Assessment and Diagnostics, Oxygen and Aerosol Therapy, Airway Management, Mechanical Ventilation, and Emergency and Critical Care); Section 2: Non-Technical Skills (covering Communication and Interpersonal Skills, Leadership and Professional Development, Ethical and Professional Practice, and Evidence-Based Practice and Research); and Section 3: Specialized Practice Areas (comprising Neonatal/Pediatric Care, Pulmonary Rehabilitation and Pulmonary Function Testing, Sleep Medicine, and Home Care).\u003c/p\u003e \u003cp\u003eA panel of six respiratory therapy experts was intentionally selected. Criteria for inclusion were at least 10 years of professional experience in respiratory therapy, active engagement in clinical practice, teaching, or supervision, and familiarity with competency-based curricula. The panel featured a diverse range of backgrounds, including adult critical care, pediatric and neonatal respiratory care, clinical education, and supervisory roles.\u003c/p\u003e \u003cp\u003eExperts evaluated each item on a 4-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree to 4\u0026thinsp;=\u0026thinsp;Strongly Agree) to assess its relevance to contemporary respiratory therapy practices in Dubai Health. Ratings of 3 or 4 indicated endorsement of relevance, following standard CVI methodology [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The Item-level CVI (I-CVI) was determined by the proportion of experts rating an item as relevant. Scale-level CVI was calculated as S-CVI/Ave (the average of all I-CVIs) and S-CVI/UA (the proportion of items with universal agreement, I-CVI\u0026thinsp;=\u0026thinsp;1.00). Items with an I-CVI below 0.78 were reviewed for possible revision or removal, as per established guidelines [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Bootstrap 95% confidence intervals for S-CVI/Ave were estimated using the bias-corrected and accelerated (BCa) method with 10,000 replications to assess sampling uncertainty. Additionally, experts provided qualitative feedback on each domain.\u003c/p\u003e\n\u003ch3\u003ePhase 2: Pilot Testing and Psychometric Evaluation\u003c/h3\u003e\n\u003cp\u003eA total of 80 licensed respiratory therapists from the three Dubai Health facilities were invited through institutional email. Of these, 58 (72.5%) completed the RT-LNI, and 45 of the 58 (77.6%) also participated in the user evaluation survey. Inclusion criteria included: (i) current licensure as a registered respiratory therapist in the UAE, (ii) at least one year of professional experience, and (ii) a minimum of 20 hours weekly in clinical practice. Exclusion criteria included roles solely administrative without clinical duties, as well as unlicensed students or trainees.\u003c/p\u003e \u003cp\u003eThe revised 75-item RT-LNI was administered using a 5-point competency rating scale based on the Dreyfus model of skill acquisition [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], where 1\u0026thinsp;=\u0026thinsp;Novice (basic awareness, needing significant guidance), 2\u0026thinsp;=\u0026thinsp;Advanced Beginner (can perform with regular supervision), 3\u0026thinsp;=\u0026thinsp;Competent (works independently in standard situations), 4\u0026thinsp;=\u0026thinsp;Proficient (adapts approach with minimal supervision), and 5\u0026thinsp;=\u0026thinsp;Expert (demonstrates mastery and can teach others). The instrument also included 22 open-ended questions to gather qualitative insights into learning needs and priorities.\u003c/p\u003e \u003cp\u003eThe user evaluation survey measured five dimensions using a 5-point Likert scale (1\u0026thinsp;=\u0026thinsp;Strongly Disagree to 5\u0026thinsp;=\u0026thinsp;Strongly Agree): clarity and usability (6 items), relevance of content (5 items), perceived value and effectiveness (5 items), feasibility (4 items), and overall satisfaction (3 items). Both instruments were distributed electronically through Microsoft Forms.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics, including means, standard deviations, and ranges, were computed for all items and domains. Internal consistency was evaluated using Cronbach's alpha for each domain and for the entire instrument, with 95% confidence intervals derived through the Fisher transformation method. Standard benchmarks guided interpretation: alpha values of 0.70 or higher are acceptable, 0.80 or higher are good, and 0.90 or higher are excellent [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Corrected item-total correlations within each domain were analyzed to identify potentially redundant or underperforming items, with those below 0.30 flagged for further review.\u003c/p\u003e \u003cp\u003eConstruct validity was evaluated using domain-level exploratory factor analysis (EFA). The adequacy of factor analysis cannot be judged solely by fixed sample size cutoffs or simple subject-to-variable ratios; instead, it depends on the strength and clarity of the underlying structure, including communalities, factor loadings, and the number of indicators per factor [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Given the pilot sample size (n\u0026thinsp;=\u0026thinsp;58), conducting a full 75-item EFA would have resulted in an unfavorable ratio of sample size to model complexity. Therefore, EFA was performed separately within each competency domain\u0026mdash;a practical pilot-stage approach that minimizes dimensional complexity and assesses the internal consistency of each theoretically defined domain [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This method is appropriate for initial instrument development as a preliminary structural review, while recognizing that within-domain analyses cannot identify cross-domain item complexity and do not replace subsequent item-level validation [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Subject-to-variable ratios for each domain analysis ranged from 4.1:1 to 14.5:1.\u003c/p\u003e \u003cp\u003ePrincipal axis factoring (PAF) with varimax rotation was used. The selection of orthogonal rotation was based on the instrument's design assumption that competency domains are conceptually distinct constructs; a sensitivity analysis using oblique (promax) rotation was also performed to evaluate solution stability given observed inter-domain correlations. The Kaiser-Meyer-Olkin (KMO) measure and Bartlett's test of sphericity were calculated for each domain. Factor retention was primarily guided by parallel analysis, supplemented by the Kaiser criterion (eigenvalue greater than 1.0) and scree plot review, in line with current methodological recommendations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Factor loadings of 0.40 or higher were deemed acceptable, with loadings above 0.70 considered strong [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Complete factor loading matrices, communalities, and corrected item-total correlations for all 13 domains are provided in Additional file 3. These exploratory findings should be viewed as preliminary; confirmatory factor analysis with a larger independent sample is needed to confirm the instrument's definitive factor structure.\u003c/p\u003e \u003cp\u003eInter-domain correlations (Spearman's rho) were calculated to evaluate discriminant validity; correlations below 0.85 indicate that the constructs are distinct.\u003c/p\u003e \u003cp\u003eMissing data were reviewed at both the item and the respondent levels. All 58 participants in Phase 2 completed all 75 Likert-scale items; no item-level missing data were found, as the electronic survey (Microsoft Forms) mandated responses to all competency items before submission. The user evaluation survey was optional; 45 of 58 participants (77.6%) completed it, and all 45 provided responses to every item. Therefore, no imputation or handling of missing data was needed. The 13 non-respondents to the evaluation survey are recognized as a potential source of non-response bias (see Limitations).\u003c/p\u003e \u003cp\u003eData distribution was evaluated for normality using the Shapiro-Wilk test. Subgroup comparisons based on years of experience (1\u0026ndash;10 vs. 11 or more years) and prior use of learning needs Instruments (yes/no) were performed with Mann-Whitney U tests. Since there were 26 planned comparisons (13 domains by 2 grouping variables), a Bonferroni correction was applied (adjusted significance threshold: p less than 0.002). Although Bonferroni is more conservative compared to alternatives like the Holm-Bonferroni step-down procedure or Benjamini-Hochberg false discovery rate control, it was chosen for its simplicity and transparency in this exploratory study, where minimizing Type I error was prioritized. Both unadjusted and Bonferroni-adjusted p-values are presented to help readers evaluate practical significance alongside statistical significance. Effect sizes for Mann-Whitney U tests were reported as rank-biserial correlations (r), interpreted as: small (0.10), medium (0.30), large (0.50) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. A post-hoc sensitivity analysis showed that with n\u0026thinsp;=\u0026thinsp;58 and alpha\u0026thinsp;=\u0026thinsp;0.002, the smallest detectable effect size at 80% power was r\u0026thinsp;=\u0026thinsp;0.52, indicating only large effects could be detected with the current sample. This reports the minimum detectable effect size rather than observed post-hoc power, aligning with recommendations that observed power calculations are uninformative because they are monotonically related to p-values [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This conservative approach was used to minimize Type I error in this exploratory analysis; however, the study was underpowered to detect small-to-medium subgroup differences, and null results should be interpreted accordingly.\u003c/p\u003e \u003cp\u003eUser evaluation data were analyzed descriptively. Open-ended responses were examined using summative content analysis: responses were read independently by two investigators (MDD, NZ), and recurring themes were identified through frequency counts. Themes mentioned by 25% or more of respondents were reported. Data were collected using Microsoft Forms, and all quantitative analyses were performed with IBM SPSS Statistics version 26.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePhase 1: Content Validation\u003c/h2\u003e \u003cp\u003eThe expert panel included 6 respiratory therapists (4 male, 2 female) with an average of 18.3 years of professional experience (SD 4.2). Panel members came from diverse national backgrounds and had been practicing in the UAE for an average of 11.6 years (SD 3.8). Five experts (83.3%) mainly worked in adult critical care, two (33.3%) also had pediatric/neonatal experience, and one had emergency medicine experience. Three (50%) held formal roles as educators or supervisors, and four (66.7%) held master\u0026rsquo;s degrees or higher.\u003c/p\u003e \u003cp\u003eContent validity analysis of the initial 83 items showed the following distribution: 54 items (65.1%) had an I-CVI of 1.00 (universal agreement), 16 items (19.3%) had an I-CVI between 0.83 and 0.99, and 13 items (15.7%) had an I-CVI below 0.78. Of these 13 items, 8 were permanently removed due to lack of consensus or redundancy. The remaining 5 items (I-CVI 0.67\u0026ndash;0.78) were significantly revised based on expert qualitative feedback.\u003c/p\u003e \u003cp\u003eScale-level content validity was excellent: S-CVI/Ave\u0026thinsp;=\u0026thinsp;0.90 (95% CI 0.86\u0026ndash;0.94), surpassing the recommended threshold of 0.83 [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The S-CVI/UA was 0.65 (54 of 83 items with perfect agreement), a level consistent with validation studies involving six or more expert raters [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Domain-level CVI results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eExpert feedback prompted several key refinements: the inclusion of lung ultrasound as an emerging skill in respiratory assessment, expansion of non-invasive ventilation content, and enhancement of ethics items related to end-of-life care. Items concerning sleep medicine received lower I-CVI scores (range 0.67\u0026ndash;0.83); however, these were kept due to the growing importance of sleep medicine in respiratory therapy and potential bias from panel composition, since all experts mainly practiced in critical care settings. The updated instrument now includes 75 items across 13 domains.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContent Validity Index Results by Domain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eI-CVI Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean I-CVI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUA n (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResp. Assessment \u0026amp; Diagnostics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (75.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen \u0026amp; Aerosol Therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (57.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirway Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMechanical Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7 (70.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency \u0026amp; Critical Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5 (62.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeadership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics \u0026amp; Professionalism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvidence-Based Practice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal/Pediatric Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary Rehabilitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3 (60.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.67\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0 (0.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.83-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4 (66.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall (83 items)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.33-1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54 (65.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePhase 2: Participant Characteristics\u003c/h3\u003e\n\u003cp\u003eOf the 80 invited respiratory therapists, 58 (72.5%) completed the RT-LNI, and 45 of these (77.6%) also completed the evaluation survey (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most were employed at Rashid Hospital (48, 82.8%), with 5 (8.6%) at Al Jalila Children's Hospital and 5 (8.6%) at Latifa Hospital. Thirty-five participants (60.3%) reported 1\u0026ndash;10 years of experience, 16 (27.6%) had 11\u0026ndash;20 years, and 7 (12.1%) had more than 20 years. Thirty-two (55.2%) held a bachelor\u0026rsquo;s degree, while 26 (44.8%) had postgraduate qualifications. The majority worked in critical care/ICU settings (42, 72.4%), followed by acute care (14, 24.1%), emergency departments (8, 13.8%), and neonatal/pediatric specialties (4, 6.9%). Forty-one participants (70.7%) reported prior exposure to learning needs Instruments. Participant characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant Characteristics (n\u0026thinsp;=\u0026thinsp;58)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRashid Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48 (82.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl Jalila Children\u0026rsquo;s Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatifa Hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (8.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35 (60.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (27.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (12.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32 (55.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePostgraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26 (44.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePractice Setting\u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical Care/ICU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42 (72.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14 (24.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency Department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8 (13.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeonatal/Pediatric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4 (6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRehabilitation/Long-term Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (5.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrior LNA Tool Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41 (70.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (29.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e\u0026dagger;Participants could select more than one practice setting; percentages sum to \u0026gt;\u0026thinsp;100%.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eReliability and Internal Consistency\u003c/h3\u003e\n\u003cp\u003eCronbach's alpha coefficients across the 13 domains ranged from 0.70 to 0.95, with a median of 0.91. Results are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Domains with excellent internal consistency (alpha 0.90 or above) included Respiratory Assessment and Diagnostics (0.94, 95% CI 0.91\u0026ndash;0.96), Oxygen and Aerosol Therapy (0.92, 95% CI 0.88\u0026ndash;0.95), Airway Management (0.95, 95% CI 0.93\u0026ndash;0.97), Mechanical Ventilation (0.93, 95% CI 0.90\u0026ndash;0.95), Emergency and Critical Care (0.94, 95% CI 0.91\u0026ndash;0.96), Evidence-Based Practice and Research (0.92, 95% CI 0.88\u0026ndash;0.95), and Sleep Medicine (0.91, 95% CI 0.86\u0026ndash;0.94). Domains with good consistency (0.80\u0026ndash;0.89) included Communication (0.84, 95% CI 0.78\u0026ndash;0.89), Leadership (0.82, 95% CI 0.75\u0026ndash;0.87), and Pulmonary Rehabilitation (0.80, 95% CI 0.72\u0026ndash;0.86). Ethics (0.70, 95% CI 0.58\u0026ndash;0.80), Professional Development (0.71, 95% CI 0.59\u0026ndash;0.80), and Home Care (0.73, 95% CI 0.62\u0026ndash;0.82) meet the acceptable threshold.\u003c/p\u003e \u003cp\u003e Corrected item-total correlations within each domain ranged from 0.38 to 0.89 (one item in the Ethics domain yielded 0.31 before domain-level factor extraction; see Additional file 3 for complete matrices). Within the Airway Management domain (alpha\u0026thinsp;=\u0026thinsp;0.95), three item pairs showed inter-item correlations exceeding 0.80, indicating potential redundancy that should be addressed in future instrument refinement to decrease respondent burden.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eConstruct Validity: Domain-Level Exploratory Factor Analysis\u003c/h2\u003e \u003cp\u003eGiven the pilot sample size (n\u0026thinsp;=\u0026thinsp;58), exploratory factor analysis was performed within each domain separately to ensure adequate subject-to-variable ratios (ranging from 4.1:1 to 14.5:1). This method assesses the unidimensionality of each domain rather than the entire factorial structure of the instrument. These findings should therefore be viewed as preliminary; a larger sample and confirmatory factor analysis are necessary to determine the full factor structure.\u003c/p\u003e \u003cp\u003ePrincipal axis factoring with varimax rotation was performed on all 13 domains. KMO values ranged from 0.64 (Home Care) to 0.88 (Mechanical Ventilation), indicating adequate to excellent sampling adequacy (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Bartlett's test of sphericity was statistically significant (p less than 0.001) for all domains, confirming sufficient inter-item correlations to support factor analysis.\u003c/p\u003e \u003cp\u003eFor each domain, a single dominant factor was extracted based on parallel analysis supplemented by the Kaiser criterion (eigenvalue greater than 1.0), with one exception: the Emergency and Critical Care domain yielded two factors, the first explaining 72.4% of the variance and the second accounting for an additional 9.1%. In the two-factor solution, Factor 1 was characterized by acute resuscitation items (CPR, rapid response, trauma care, ACLS; loadings 0.78\u0026ndash;0.94), while Factor 2 was defined by prolonged critical care management items (hemodynamic monitoring, transport, sepsis recognition; loadings 0.61\u0026ndash;0.72). Loadings on the primary factor ranged from 0.52 to 0.96 across all domains (median 0.79). Three items across the instrument had primary factor loadings between 0.40 and 0.52; no items fell below 0.40. Corrected item-total correlations ranged from 0.38 to 0.89. The cumulative variance explained by the extracted factors ranged from 67.8% to 91.0%. A sensitivity analysis using oblique (promax) rotation produced consistent factor structures across all 13 domains. Complete factor loading matrices, communalities, and corrected item-total correlations for all 13 domains are provided in Additional file 3.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and Construct Validity by Domain\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eα (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKMO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVar %\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLoading Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS:V\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResp. Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.91\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.68\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.3:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen \u0026amp; Aerosol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.71\u0026ndash;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.3:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirway Mgmt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95 (0.93\u0026ndash;0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.82\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.7:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMech. Vent.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93 (0.90\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.62\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.8:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency \u0026amp; CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.91\u0026ndash;0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e81.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u0026ndash;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.3:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommunication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84 (0.78\u0026ndash;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.59\u0026ndash;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.6:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeadership \u0026amp; PD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.82 (0.75\u0026ndash;0.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.55\u0026ndash;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.6:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.70 (0.58\u0026ndash;0.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e67.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.52\u0026ndash;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.5:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBP \u0026amp; Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92 (0.88\u0026ndash;0.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.70\u0026ndash;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.7:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeo/Peds\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89 (0.84\u0026ndash;0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.64\u0026ndash;0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.5:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulm Rehab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80 (0.72\u0026ndash;0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e71.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.5:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.86\u0026ndash;0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.74\u0026ndash;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.5:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.73 (0.62\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.61\u0026ndash;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.5:1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiscriminant Validity\u003c/h2\u003e \u003cp\u003eInter-domain Spearman correlations ranged from 0.22 to 0.78, with a median of 0.51. The highest correlation was found between Mechanical Ventilation and Emergency and Critical Care (rho\u0026thinsp;=\u0026thinsp;0.78), which is expected due to the clinical overlap between these domains. No inter-domain correlation exceeded 0.85, supporting the distinctiveness of the 13 domain constructs [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLearning Needs Patterns\u003c/h2\u003e \u003cp\u003eParticipants reported the highest self-assessed competence in core technical skills relevant to tertiary care practice: Airway Management (mean 3.9, SD 0.6), Mechanical Ventilation (mean 3.8, SD 0.7), Oxygen and Aerosol Therapy (mean 3.8, SD 0.5), and Emergency and Critical Care (mean 3.7, SD 0.6). Non-technical skills were rated at moderate levels: Communication (mean 3.3, SD 0.8), Ethics and Professionalism (mean 3.2, SD 0.7), Professional Development (mean 3.1, SD 0.8), and Leadership (mean 3.0, SD 0.9). Domain-level competency scores are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe lowest self-assessed competence, indicating priority learning needs, was seen in Evidence-Based Practice and Research (mean 2.4, SD 1.1), Sleep Medicine (mean 2.6, SD 1.2), Pulmonary Rehabilitation (mean 2.7, SD 1.0), and Neonatal/Pediatric Care (mean 2.8, SD 1.1). These areas are less often encountered in the mostly adult critical care settings where most participants worked.\u003c/p\u003e \u003cp\u003eAnalysis of open-ended responses (n\u0026thinsp;=\u0026thinsp;42 respondents providing at least one response) revealed recurring themes. Research and evidence-based practice, specifically critical appraisal skills and understanding of research design, were identified as a priority by 23 respondents (54.8%). Sleep medicine training was mentioned by 15 respondents (35.7%), and pulmonary rehabilitation by 11 (26.2%). Non-technical skill development, particularly conflict resolution and leadership in resource-constrained environments, was noted by 22 respondents (52.4%).\u003c/p\u003e \u003cp\u003eSubgroup comparisons using Mann-Whitney U tests with Bonferroni correction (adjusted alpha\u0026thinsp;=\u0026thinsp;0.002) showed no statistically significant differences in competency ratings between experience groups (1\u0026ndash;10 years vs. 11 or more years) or between those with and without prior LNA tool use (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The largest unadjusted effect was observed for Evidence-Based Practice by experience group (unadjusted p\u0026thinsp;=\u0026thinsp;0.06, rank-biserial r\u0026thinsp;=\u0026thinsp;0.18), where more experienced RTs rated slightly higher, but this did not remain significant after correcting for multiple comparisons. All effect sizes were negligible to small (rank-biserial r\u0026thinsp;=\u0026thinsp;0.02\u0026ndash;0.21), well below the threshold for medium effects (r\u0026thinsp;=\u0026thinsp;0.30). Post-hoc power analysis confirmed that with n\u0026thinsp;=\u0026thinsp;58 and alpha\u0026thinsp;=\u0026thinsp;0.002, only large effects (r\u0026thinsp;\u0026ge;\u0026thinsp;0.52) could be detected with 80% power, indicating that the analysis was underpowered to find small-to-medium subgroup differences. These results are therefore consistent with either a true similarity in self-assessed learning needs across subgroups or insufficient power to detect existing differences; a definitive subgroup analysis will require sample sizes of at least 100 per group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelected Subgroup Comparisons by Experience Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDomain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMdn [IQR] 1\u0026ndash;10 yrs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMdn [IQR] 11\u0026thinsp;+\u0026thinsp;yrs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep (unadj.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep (Bonf.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEBP \u0026amp; Research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2 [1.5-3.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.7 [1.8\u0026ndash;3.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep Medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5 [1.8\u0026ndash;3.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.8 [2.0-3.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMech. Vent.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 [3.3\u0026ndash;4.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.9 [3.5\u0026ndash;4.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAirway Mgmt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.9 [3.5\u0026ndash;4.3]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.0 [3.6\u0026ndash;4.4]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e4 of 13 domains shown, selected to illustrate the range from largest to smallest observed effects.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUser Evaluation and Acceptability\u003c/h2\u003e \u003cp\u003eUser evaluation surveys were completed by 45 of 58 participants (77.6%). Results showed strong acceptability across all dimensions (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Clarity and usability (6 items) had a combined mean of 25.4/30 (84.8%, with a mean of 4.23/5 per item). Content relevance (5 items) had an average of 4.30/5 per item. Perceived value and effectiveness (5 items) scored 4.28/5. Reasibility (4 items) received a mean of 4.31/5. Overall satisfaction (3 items) rated 4.20/5. The overall mean across all evaluation items was 4.26/5 (85.3% of the maximum score).\u003c/p\u003e \u003cp\u003eRespondents estimated the completion time at 15\u0026ndash;20 minutes and showed willingness to use the tool annually. Open comments expressed appreciation for the tool's thorough coverage of both technical and non-technical skills, the inclusion of emerging competencies like lung ultrasound, and the value of structured self-reflection for professional growth planning.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUser Evaluation Results (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCum. Mean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean/Item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% Max\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClarity \u0026amp; Usability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.4 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelevance of Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.5 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.4 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeasibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.2 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Satisfaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.6 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.1 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e85.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSummary of Key Findings\u003c/h2\u003e \u003cp\u003eThis study introduces the RT-LNI, an instrument built around competencies, specifically made for respiratory therapists. According to a thorough search of PubMed, CINAHL, and Scopus, it appears to be the first evidence-based, profession-specific assessment of learning needs for respiratory therapy. The RT-LNI shows strong content validity, good to excellent internal consistency, initial support for construct validity at the domain level, and high acceptance among users. In addition to its psychometric strengths, the tool reveals meaningful patterns of self-rated competence that directly impact the quality of respiratory care and guide professional development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eContent Validity\u003c/h2\u003e \u003cp\u003eThe S-CVI/Ave of 0.90 (95% CI 0.86\u0026ndash;0.94) surpasses the recommended thresholds by Lynn [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Polit and Beck [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] for excellent content validity. This result is comparable to similar validation studies of instruments in allied health fields; for instance, Gaspard and Yang [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] found an S-CVI/Ave of 0.87 for a training needs Instrument in Saint Lucia, while Bartholomew et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] reported an S-CVI/Ave of 0.89 for an allied health workforce needs assessment in Australia. The universal agreement index (S-CVI/UA\u0026thinsp;=\u0026thinsp;0.65) aligns with studies using panels of six or more experts, where achieving perfect consensus is inherently more challenging [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe lower CVI scores for sleep medicine items highlight an important methodological issue: the composition of the expert panel can systematically affect content validation results. The mostly critical care-focused panel may have underestimated skills outside their routine practice. Future content validation efforts should include experts from all relevant areas being assessed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePsychometric Properties\u003c/h2\u003e \u003cp\u003eInternal consistency, indicated by Cronbach's alpha values ranging from 0.70 to 0.95, was acceptable to excellent across all domains. However, the very high alpha coefficients in some technical areas, such as Airway Management (alpha\u0026thinsp;=\u0026thinsp;0.95), should be interpreted with caution. While high reliability is beneficial, alpha values above 0.95 may suggest item redundancy, a narrow definition of the construct, or ceiling effects within those domains [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Notably, the top-scoring domains, Airway Management (mean 3.9/5) and Mechanical Ventilation (mean 3.8/5), exhibited compressed score distributions at the upper end of the Dreyfus scale, indicating potential ceiling effects that might artificially inflate internal consistency by limiting response variability. Testing in settings with a broader range of skills could better distinguish higher levels of competency. Within the Airway Management domain, three item pairs had correlations (r) greater than 0.80: artificial airway insertion, endotracheal intubation assistance, and tracheostomy care. These items address distinct procedures, oral/nasal intubation, surgical airway management, and tracheostomy maintenance, and were kept because they represent separate competency components per the AARC and CSRT frameworks. Future revisions should assess whether responses can differentiate among these items, or whether combining them into a single \"advanced airway procedures\" item might reduce respondent burden without losing essential content.\u003c/p\u003e \u003cp\u003eDomain-level exploratory factor analysis provided initial evidence of construct validity. Methodological research shows that the adequacy of EFA depends less on fixed sample size thresholds and more on the quality of the factor model, such as strong communalities, clear loadings, and adequate indicators per factor, that can support interpretable results even with relatively small samples [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, each domain mostly exhibited a unidimensional structure, with strong factor loadings (median 0.79, range 0.52\u0026ndash;0.96) and high communalities, confirming the internal consistency of the competency domains under favorable structural conditions. The only exception was the Emergency and Critical Care domain, which revealed a two-factor solution: Factor 1 (72.4% of variance) encompassed competencies related to acute resuscitation and emergency stabilization (CPR, rapid response, trauma, ACLS), while Factor 2 (additional 9.1% variance) included prolonged critical care management skills (hemodynamic monitoring, transport, sepsis management). This clinically meaningful split suggests that the Emergency and Critical Care domain may comprise two related but distinct sub-constructs, a finding that should inform future domain refinement either through formal splitting or item revision to enhance unidimensionality.\u003c/p\u003e \u003cp\u003eA sensitivity analysis using oblique (promax) rotation yielded factor structures consistent with the varimax solution across all 13 domains, with factor correlations within rotated solutions remaining below 0.40, supporting the appropriateness of orthogonal rotation for this instrument. KMO values ranged from 0.64 (Home Care, Ethics) to 0.88 (Mechanical Ventilation); the two domains with marginal KMO values (below the conventional 0.70 threshold) each contained only four items, and their factor solutions showed strong loadings and sufficient variance explained, aligning with evidence that KMO thresholds are less strict when there are few items per factor and loadings are strong [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These marginal KMO values should be interpreted with caution given the small sample; KMO itself is sensitive to sample size, and values in the 0.60\u0026ndash;0.70 range at n\u0026thinsp;=\u0026thinsp;58 do not preclude interpretable factor solutions but do suggest that replication with larger samples is necessary to confirm stability. Nevertheless, these exploratory results should be interpreted cautiously. Confirmatory factor analysis with a larger independent sample (n greater than or equal to 300) is crucial to test the measurement model and analyze cross-domain item complexity that within-domain analyses cannot identify [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInter-domain correlations (Spearman's rho 0.22\u0026ndash;0.78) support the discriminant validity of the 13-domain structure. The highest observed correlation (Mechanical Ventilation and Emergency Critical Care, rho\u0026thinsp;=\u0026thinsp;0.78) is expected clinically, given the overlap in competencies required for these closely related practice areas. No correlation exceeded the 0.85 threshold that would indicate domain redundancy [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications of Learning Needs Findings\u003c/h2\u003e \u003cp\u003eThe identified learning needs patterns from the RT-LNI have significant implications for improving respiratory care quality and workforce growth. When viewed through the Dreyfus model underlying the RT-LNI response scale, a mean score of 2.4 out of 5 for evidence-based practice (EBP) aligns with the \"Advanced Beginner\" stage, suggesting that RTs see themselves as capable of performing EBP tasks with regular supervision but not independently. Conversely, core technical skills scored between 3.7 and 3.9 out of 5, corresponding to the \"Proficient\" stage, indicating independent practice with situational adjustments. The approximately 1.5-stage gap between EBP and core technical skills on the Dreyfus scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) highlights a clinically important disparity that can directly impact the quality of respiratory care provided.\u003c/p\u003e \u003cp\u003eThe EBP finding aligns with international surveys of respiratory therapists. Zaccagnini et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] identified research knowledge and evidence-based practice as priority gaps among Canadian RTs, and Clark et al. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] reported limited self-efficacy in evidence-based practice skills among US respiratory therapists. The recent development of a Scholarly Practice Tool for Canadian RTs [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] further emphasizes the profession's recognition that scholarly and EBP competencies need dedicated assessment and development. These consistent findings across various healthcare systems point to a systemic gap in RT education and CPD programs, which may impact the adoption of evidence-based respiratory care, patient outcomes, and the profession's ability to engage in practice-based research [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSimilarly, the identified learning needs in sleep medicine (mean 2.6/5) and pulmonary rehabilitation (mean 2.7/5), both at the upper \"Advanced Beginner\" level, are directly relevant to the quality of respiratory care services. These scores probably reflect limited clinical exposure rather than a lack of ability; 72.4% of participants mainly worked in ICU settings where sleep medicine and rehabilitation are rarely encountered. These specialties are growing areas of RT practice that may be underserved by current educational programs [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Systematic identification of these gaps using validated tools like the RT-LNI can help institutions make evidence-based decisions about CPD investment priorities, potentially enhancing the quality and scope of respiratory care services.\u003c/p\u003e \u003cp\u003eThe moderate ratings in non-technical competencies, especially leadership (average 3.0/5, labeled as \"Competent\") and professional development (average 3.1/5), align with emerging research on the importance of non-technical skills in interprofessional team function and patient safety [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The open-ended response data, where 52.4% of respondents identified non-technical skill development as a priority, offers supporting evidence that the RT-LNI reflects meaningful perceptions of learning needs that practitioners themselves recognize as important. Using Kern's six-step educational design framework [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], these findings directly inform Step 2 (targeted needs assessment): a CPD program aimed at closing the EBP gap might focus on advancing from \"Advanced Beginner\" to \"Competent\" through structured evidence synthesis workshops, journal club participation, and mentored quality improvement projects. The RT-LNI would serve both as the initial assessment and as the measure of CPD effectiveness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSelf-Assessment Validity Considerations\u003c/h2\u003e \u003cp\u003eThe RT-LNI depends on self-assessment of competence, and interpreting its results must consider extensive evidence on the accuracy of self-assessment in health professions. Systematic reviews and meta-analyses consistently show weak to moderate alignment between self-assessed and objectively measured competence [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This miscalibration follows a well-known pattern: lower performers tend to overestimate their abilities, while higher performers may underestimate [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], a trend confirmed in recent studies involving medical students [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], surgical residents [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and endoscopy practitioners [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Notably, self-assessment accuracy varies by domain. Procedural skills tend to be better calibrated than knowledge-based or non-technical skills [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and by experience level, with more experienced clinicians showing closer agreement with external ratings [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For the RT-LNI, this indicates that the learning gaps identified in areas like EBP (average 2.4/5) may underestimate actual needs, especially among less experienced RTs who might lack the metacognitive awareness to recognize their own limitations.\u003c/p\u003e \u003cp\u003eHowever, the development of self-assessment research since the mid-2000s offers important context for understanding these findings. The field has shifted from questioning whether practitioners can accurately self-assess in theory to creating systems that support informed self-assessment and self-judgment that incorporate external feedback, explicit standards, observation, and reflection [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Sargeant et al. [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] argued that self-assessment becomes educationally valuable when it is integrated into such systems rather than viewed as an isolated personal skill. Growing evidence indicates that structured self-assessment with clear standards and feedback can help identify learning needs and promote self-regulated improvement, even though retrospective global self-ratings are still poor tools for summative judgment [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. This distinction is critical: the RT-LNI is designed to identify formative learning needs, not to certify summative competency. Its alignment with AARC and CSRT/NCF competency frameworks provides the normative benchmark that turns isolated self-rating into informed self-assessment.\u003c/p\u003e \u003cp\u003eFurthermore, the non-significant differences between the experience groups (1\u0026ndash;10 vs. 11\u0026thinsp;+\u0026thinsp;years) should be interpreted in light of the existing literature. Although the small sample size (n\u0026thinsp;=\u0026thinsp;58) restricts the ability to detect subgroup differences, the small effect sizes observed (r\u0026thinsp;=\u0026thinsp;0.02\u0026ndash;0.18) align with two possible explanations: either there is genuine similarity in perceived learning needs across experience levels, or there is differential miscalibration, where less experienced RTs overestimate their competence while more experienced RTs underestimate, resulting in artificially similar group averages. Clarifying these possibilities requires future research that compares RT-LNI self-assessments with objective competency measures such as simulation assessments, clinical audits, multi-source feedback, or knowledge testing [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, this single-site study conducted within Dubai Health restricts the generalizability of the findings, as the scope of RT practice varies between countries. Second, although the pilot sample size (n\u0026thinsp;=\u0026thinsp;58) is adequate for initial psychometric evaluation, it is too small for confirmatory factor analysis or detailed subgroup comparisons; thus, EFA results need replication. Third, the cross-sectional design prevents assessment of test-retest reliability. Fourth, self-assessment may be affected by systematic biases that impact accuracy. Fifth, the primarily critical care expert panel might have introduced specialty bias into CVI ratings. Sixth, the open-access survey link could lead to duplicate responses, although this risk is reduced by anonymous, voluntary participation. Seventh, the optional user evaluation (77.6% response rate) may introduce non-response bias, potentially inflating satisfaction ratings. Eighth, participant demographics did not include gender or nationality, which limits the ability to analyze differential item functioning in this diverse workforce; future studies should gather these variables. Finally, administering the survey in English only might influence interpretation among Dubai's multicultural RT community, where practitioners from South Asian, Arab, and African expatriate backgrounds may interpret Dreyfus-anchored scale items differently across cultural contexts. Developing an Arabic translation and cultural adaptation is the next essential step.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eFuture Directions\u003c/h2\u003e \u003cp\u003eFuture research should focus on three key areas. First, conducting multisite validation with diverse RT populations across various healthcare systems and regions is crucial, allowing confirmatory factor analysis with sufficient sample sizes (n\u0026thinsp;\u0026gt;\u0026thinsp;300) and testing measurement invariance across settings. Additionally, establishing test-retest reliability is important to evaluate score stability over clinically relevant timeframes.\u003c/p\u003e \u003cp\u003eSecond, responsiveness, the instrument's ability to detect meaningful change following CPD interventions, is a crucial next step for establishing the RT-LNI's usefulness within Kern's educational design framework. Existing evidence supports the feasibility of this approach: validated CPD self-report tools, such as the CPD-REACTION [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and the SE-12 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], have demonstrated sensitivity to educational change in pre-post studies. Responsiveness assessment for the RT-LNI should use a pre-post design focused on specific CPD activities (e.g., EBP workshops for RTs at the \"Advanced Beginner\" level), with effect sizes, reliable change indices, and follow-up behavioral data to determine whether score changes indicate genuine learning. Establishing a minimal clinically important difference (MCID) on the Dreyfus scale, for example, whether a one-point shift from \"Advanced Beginner\" to \"Competent\" signifies meaningful progress, would enhance interpretability. Current measurement guidance views responsiveness as longitudinal validity demonstrated through hypothesis-driven change analyses rather than requiring a pre-established MCID [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThird, convergent validity should be assessed by comparing RT-LNI self-assessments with objective competency measures such as simulation-based assessments, clinical audit data, multi-source feedback from supervisors and peers, and knowledge testing [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This would clarify the relationship between self-perceived and objectively measured learning needs and help calibrate the self-assessment results. Developing Arabic-language versions and specialty-specific modules (e.g., neonatal/pediatric, sleep medicine) would increase applicability across the UAE's diverse RT workforce and beyond.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThe Respiratory Therapy Learning Needs Instrument (RT-LNI) offers the first published, preliminary validation evidence for a competency-based self-Instrument specifically created for respiratory therapists. The tool shows strong content validity (S-CVI/Ave\u0026thinsp;=\u0026thinsp;0.90), acceptable to excellent internal consistency across all 13 competency domains (Cronbach's alpha 0.70\u0026ndash;0.95), initial construct validity evidence at the domain level, and high user acceptability (overall mean 4.26/5).\u003c/p\u003e \u003cp\u003eThe RT-LNI identified significant patterns of learning needs, highlighting evidence-based practice, sleep medicine, and pulmonary rehabilitation as key areas for targeted ongoing professional development. These findings are directly relevant to improving the quality of respiratory care services and planning workforce development.\u003c/p\u003e \u003cp\u003eWhile these initial findings are promising, multi-site validation with larger and more diverse samples is necessary before recommending the RT-LNI for routine clinical use. Future research should include confirmatory factor analysis, test-retest reliability evaluations, responsiveness studies, and integration with objective competency measures to strengthen the validity evidence base.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics Approval and Consent to Participate\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Mohammed Bin Rashid University of Medicine and Health Sciences Institutional Review Board (IRB reference 202400216, approval date March 2025). Site-specific approvals were obtained from Rashid Hospital, Al Jalila Children\u0026apos;s Hospital, and Latifa Hospital. Electronic informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003eConsent for Publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of Data and Materials\u003c/p\u003e\n\u003cp\u003eThe RT-LNI instrument is provided in Additional file 4. The datasets analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompeting Interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency, commercial entity, or not-for-profit organization.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Author Contributions\u003c/p\u003e\n\u003cp\u003eMDD conceptualized and designed the study, developed the RT-LNI instrument, collected and analyzed data, and drafted the manuscript. LP contributed to study design, instrument refinement, and critical revision of the manuscript. AHK contributed to the study design and analysis, and critical revision of the manuscript. NZ supervised the research, contributed to study design and instrument development, and drafted and critically revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors thank the six respiratory therapy experts who participated in content validation and the 58 respiratory therapists at Dubai Health who participated in pilot testing.\u0026nbsp;\u003cbr clear=\"all\"\u003e \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eClark KM, Brown P, Gill D, Karper W. Assessing Evidence-Based Practice Knowledge, Self-Efficacy, and Use Among Respiratory Therapists. Respir Care. 2024;69(8):913\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSreedharan JK, Rao UK, Al Ahmari M, Kotian SM, Mokshanatha PB. Validation of a structured questionnaire to assess the perception and satisfaction of respiratory therapy students toward career prospects and learning resources. 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New York: McGraw-Hill; 1994.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"respiratory therapy, learning needs assessment, competency-based assessment, professional development, content validity, self-assessment, instrument development, United Arab Emirates","lastPublishedDoi":"10.21203/rs.3.rs-9484867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9484867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eRespiratory therapists (RTs) need continuous professional development to stay competent in evolving clinical areas. However, there is no validated, professional-specific tool for systematically evaluating their learning needs in respiratory therapy. This study aimed to create and preliminarily validate the Respiratory Therapy Learning Needs Instrument (RT-LNI), a self-assessment based on key competency frameworks.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A two-phase cross-sectional study was carried out at three Dubai Health tertiary care centers. In the first phase, six expert RTs assessed 83 items across 13 competency domains using the content validity index (CVI) methodology. During the second phase, 58 licensed RTs were invited and 50 completed the refined 75-item instrument (response rate of 72.5%), along with a user evaluation survey completed by 45 participants. Psychometric evaluation included Cronbach's alpha and domain-level exploratory factor analysis (principal axis factoring with varimax rotation; promax sensitivity analysis). The number of factors was determined by parallel analysis. Subgroup comparisons employed Mann-Whitney U tests with Bonferroni correction. The reporting adhered to the STROBE checklist.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eContent validation demonstrated high validity, with an S-CVI/Ave of 0.90 (95% CI 0.86\u0026ndash;0.94). After removing eight items, the instrument consisted of 75 items. Internal consistency ranged from acceptable to excellent (Cronbach's alpha 0.70\u0026ndash;0.95; median 0.91). Domain-level factor analysis mostly indicated unidimensional structures (KMO 0.64\u0026ndash;0.88; median primary factor loading 0.79), except in Emergency and Critical Care, which showed a two-factor structure\u0026mdash;acute resuscitation versus prolonged management. Self-assessed competence was highest in core technical skills (mean 3.7\u0026ndash;3.9/5, corresponding to Dreyfus \"Proficient\" stage) and lowest in evidence-based practice (2.4/5), sleep medicine (2.6/5), and pulmonary rehabilitation (2.7/5), at the \"Advanced Beginner\" stage. No significant subgroup differences emerged after Bonferroni correction, although the study was underpowered for small-to-medium effects. User feedback indicated strong acceptability, with a mean score of 4.26/5 (85.3%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe RT-LNI demonstrated strong content validity, acceptable psychometric properties, and high user acceptability in this preliminary validation. As the first published, psychometrically evaluated, profession-specific learning needs assessment instrument for respiratory therapy, the tool fills a gap in the CPD infrastructure. The identified learning needs patterns have direct implications for targeted workforce development. Multi-site confirmatory validation with larger samples and convergent validity testing against objective measures are the next crucial steps.\u003c/p\u003e","manuscriptTitle":"Development and Initial Validation of the Respiratory Therapy Learning Needs Instrument (RT-LNI): A Competency-Based Self-Assessment Instrument","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:41:31","doi":"10.21203/rs.3.rs-9484867/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"44678c55-7b7c-428f-a150-1be86da96ba6","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:41:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:41:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9484867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9484867","identity":"rs-9484867","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}