Facilitators and barriers to using neurological outcome measures in different income level countries
Bibliographic record
Abstract
Background: In physiotherapy, the use of standardized outcome measures is crucial to assess individuals with neurological deficits, as part of good clinical practice. However, use of outcome measures may vary between countries of different income levels. Aim: To identify and compare facilitators and barriers related to the use of neurological outcome measures in physical therapy practice in lower middle- and high-income countries. Methods: A self-administered web-based questionnaire on the factors influencing the use of outcome measures was sent by e-mail to physical therapists working in neurology in Canada and India. The questionnaire consisted of 16 questions about facilitators and barriers to the use of neurological outcome measures. Frequencies and proportions (%) of responses to each question were computed. Differences between countries were assessed using two-proportion z-tests. Results: For India and Canada, the main facilitators of using outcome measures were similar: known reliability and validity, recommended in clinical practice guidelines, learned during professional training, and rapid and ease to administer. For both countries, lack of assessment time was identified as the most important barrier to using standardized outcome measures. Differences between countries were also noted in the barriers limiting the use of standardized outcome measures: cost and availability for India, and lack of knowledge on the selection and administration of an outcome measure for Canada. Conclusions: The identification of the factors influencing to the use of outcome measures by physical therapist working in neurology practice could help to tailor implementation strategies.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".