Validation of a structured questionnaire to assess the perception and satisfaction of respiratory therapy students toward career prospects and learning resources
Bibliographic record
Abstract
Background: Respiratory therapy is an emerging profession that has existed in India since 1995. Respiratory therapy students will play a significant role in strengthening various aspects of healthcare in the future. There are no validated instruments to evaluate students' perceptions of their careers and satisfaction with the learning resources. The primary objective of the current study is to develop and validate a structured questionnaire (SQ) for respiratory therapy students in India, encompassing all the components of their career development and satisfaction. Methods: Based on the literature review and content validity from respiratory therapy experts through multiple focused group discussions, a reliable SQ was generated with 40 items based on the Likert scale. After getting institutional ethics clearance and informed consent, the SQ was administered to 904 respiratory therapy students across the country. We performed principal component analysis (PCA), structural equation modeling, and confirmatory factor analysis (CFA) for the global fit. Cronbach's alpha was performed to estimate the internal consistency. Results: The PCA generated a 4-factor model, and internal consistency for the total scale exceeded the standard criterion of >0.70. Satisfactory goodness of fit data were yielded from CFA. Average variances extracted were higher than the correlation coefficients of the factors, which show sufficient discriminant validity. Conclusion: This study shows a clinically acceptable model, it fits and suggests the possibility of applying a SQ to a respiratory therapy student with relatively good construct validity and internal consistency, based on the results of CFA.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".