Continuing education in physical rehabilitation using Internet-based modules
Bibliographic record
Abstract
A rehabilitation outreach team created evidence-based and peer-reviewed educational modules using standard desktop presentation software. Eighteen modules on various topics in physical rehabilitation were published in several formats, including Web pages, directly from the presentation file, for the benefit of health-care providers in eastern and north-eastern Ontario. An online evaluation form could be completed by anyone visiting the Website; the people responsible for community rehabilitation services were asked to encourage their staff to complete the forms. A total of 174 forms were received. The module 'Principles of transfers for health-care workers' accounted for 18% of the evaluations and the module 'Assisted range-of-motion exercises for arms and legs to maintain joint flexibility' accounted for 14%. Thirty-nine per cent of respondents were registered nurses, 13% were physiotherapists and 26% were people with disabilities. Thirty per cent of the participants had limited or no experience with online learning. In the evaluation, high ratings were given for satisfaction and usefulness. From an educator's perspective, multimedia content could be created and distributed without a substantial investment in equipment, software, training and publication time; this represents a 'write once, publish everywhere' approach.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".