The Identification, Selection, and Validation of Topic Areas for a Physical Therapy Critical Care Learning Needs Assessment Tool: A Mixed Method Approach
Bibliographic record
Abstract
Purpose: To identify, select, and validate topic areas for a physical therapy critical care learning needs assessment tool. Method: A scoping review was used to identify knowledge/skill areas relevant to physiotherapy intensive care unit (ICU) practice and develop a survey questionnaire. Physiotherapists rated the relevance of each survey item for practice in the ICU, as well as their knowledge about each item. Descriptive statistics summarized the responses. A statistically based algorithm was used to identify highly relevant items where the level of knowledge/skill was relatively low. Experienced physiotherapists were consulted to agree on the items that should be included. The process was repeated in three additional ICUs to validate the selected items. Results: A total of 238 items, identified from 40 articles, were included in the survey and rated by physiotherapists. From survey results, a statistical-based algorithm identified 113 important topic areas for inclusion (47.48%). A modified triage technique was used to further reduce the selected areas to 90 topics. The validation process further refined the topics to 94 topics along with 13 additional site-specific topic areas. Conclusions: Topics identified in this study will inform the development of a physical therapy critical care learning needs assessment tool. Our survey tool and methodological approach could guide the development of hospital-specific learning needs assessment tools in other clinical settings.
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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 | 0.290 | 0.369 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.024 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".