Weighing the Evidence: Clinical Decision Making in Neurological Physical Therapy
Bibliographic record
Abstract
Purpose: The purpose of this study was to explore (1) the processes that neurological physical therapists go through in making dayto-day clinical decisions and (2) the sources of information or evidence that therapists use in their day-to-day practice. Method: A qualitative, grounded theory method was used consisting of semistructured, face-to-face in-depth interviews with 12 neurological physical therapists practising in a variety of settings in the Greater Toronto Area. Data were analyzed using a constant comparative approach. Results: Therapists weigh a variety of informal and formal sources of evidence as they go about their day-to-day practice. Our results fall into three main categories: (1) sources of evidence weighed, (2) when and why therapists weigh the evidence, and (3) factors that influence the process of weighing the evidence. Conclusions: The results of this study indicate that implementing evidence-based practice will be a challenge to the physical therapy profession unless we broaden our definition of evidence to include informal sources of evidence that are highly valued by clinicians.
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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 | 0.337 | 0.508 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.010 | 0.030 |
| Scholarly communication | 0.026 | 0.021 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.010 | 0.010 |
| 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".