Service delivery trends for a physical rehabilitation outreach program
Bibliographic record
Abstract
PURPOSE: Evaluate clinical service characteristics of a physical rehabilitation outreach clinic. METHOD: A retrospective analysis on service-related factors and patient information (every clinical encounter between 1983 and 2002) was performed on the Terry Fox Mobile Clinic. The Terry Fox Mobile Clinic provided multidisciplinary physical rehabilitation outreach services for adults with disabilities in Eastern and North Eastern Ontario, Canada (on-site visits by rehabilitation specialists). Subjects include 4816 clients with physical disabilities from Eastern and North Eastern Ontario, Canada. Patient data (age, residence, diagnosis, inpatient/outpatient), clinic data (type, location, totals), team data (clinical disciplines), and assistive device utilization over the last 15 years of data were used for analysis. RESULTS: Over 15 years, 4816 clients (1032 clinic days, 1.46 patient-contact ratio) were seen by the outreach team. Single discipline days were the best 'number of clinic days' predictor. Following 1999, the 35-64 age group became predominate (instead of over-65). Most patients lived in their own residence. Stroke, amputations, neck and back pain, and cerebral palsy accounted for 53% of patient diagnoses. Wheelchairs and orthoses were the highest ranked assistive device consultations/prescriptions (total = 6304). Physicians were involved with the majority of patient contacts. Most clinic activity occurred at sites within 100 km driving distance. Sites over 200 km away had the lowest correlation with the number of clinic days. CONCLUSIONS: The 15 years of physical rehabilitation outreach service data is valuable for planning new outreach programs, benchmarking existing services, and telerehabilitation comparisons. Multidisciplinary physical rehabilitation can make a positive contribution to healthcare that extends beyond direct patient contact time.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".