Personalizing rehabilitation for individuals with musculoskeletal impairments: Feasibility of implementation of the Measures Associated to Prognostic (MAPS) tool
Bibliographic record
Abstract
INTRODUCTION: tic (MAPS) tool is a standardized questionnaire that integrates validated prognostic tools to detect the presence of biopsychosocial prognostic factors in patients consulting for musculoskeletal disorders. PURPOSE: The objectives were to assess the: 1) feasibility of implementation of the MAPS tool, 2) clinicians' acceptability of the dashboard, and 3) patients' acceptability of the MAPS tool. METHODS: Twenty physiotherapists and two occupational therapists from seven outpatient musculoskeletal clinics were recruited to implement the MAPS tool during a 3-month timeframe, where new patients completed the questionnaire upon initial assessment. The results were presented to the clinicians via a dashboard. Surveys and semi-structured interviews were conducted to measure feasibility and acceptability. RESULTS: Six out of 11 feasibility criteria (55%) and 21 out of 24 acceptability criteria (88%) reached the a priori threshold for success. The interviews allowed us to identify three main themes to facilitate implementation: 1) limiting the burden, 2) ensuring patients' understanding of the tool's purpose, and 3) integrating the dashboard as a clinical information tool. CONCLUSION: Our quantitative and qualitative results support the feasibility of implementation and acceptability of the MAPS tool pending minor adjustments. Depicting the patients' prognostic profile has the potential to help clinicians optimize their interventions for patients presenting with musculoskeletal disorders.
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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 | 0.045 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".