A qualitative study of healthcare provider perspectives on measuring functional outcomes in geriatric rehabilitation
Bibliographic record
Abstract
OBJECTIVES: To explore, from the perspective of healthcare providers, the barriers to and facilitators of using standardized outcome measures of physical function in geriatric rehabilitation settings. METHODS: In-depth semi-structured interviews with medical doctors, nurses, physiotherapists and occupational therapists were conducted from three geriatric rehabilitation hospitals in Ontario. Qualitative content analysis through an inductive approach was used to identify barriers to and facilitators of outcome measurement in geriatric rehabilitation settings. RESULTS: A total of 20 healthcare providers from different disciplines were interviewed. Barriers are standardized outcome measures that (1) cannot be used in isolation to inform clinical decision-making for older patients, (2) are difficult to complete and interpret in older complex patients, (3) may not be useful for all members of the clinical team and (4) are used for reasons unrelated to patient care. Facilitators are to (1) divide the standardized outcome measure into components that are useful for each team member's practice, (2) encourage standardized outcome measures as tools for improving daily communication and (3) provide adequate education and support for their daily use. CONCLUSION: This study revealed important barriers to using standardized outcome measures in geriatric rehabilitation from the perspective of healthcare providers. However, it also identified multiple factors that may help facilitate their use.
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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 | 0.044 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".