Principes d'attribution des lève-personnes et des toiles en ergothérapie
Bibliographic record
Abstract
The occupational therapist is frequently involved in the allocation process of lifting devices for clients with severe physical disabilities who are living in the community. The aim of this paper is to introduce a conceptual framework to help therapists prescribe lifting devices, including the slings. First, factors influencing the decision to prescribe such an aid are analysed based on the concepts of the Canadian Model of Occupational Performance (Canadian Association of Occupational Therapists, 1997). When working with clients toward maximizing their transfer skills, occupational therapists will take into account different aspects such as the characteristics of the client, the environment and the equipment, as well as the time allocated to complete the activity. Secondly, the notion of the work situation outlined by an organization specializing in work safety measures is used as a guide for transfer evaluation. From this viewpoint, the introduction of the lifting device occurs along a continuum of progressive loss of independence and is determined by degrees of personal independence, human assistance as well as technical assistance required to perform transfers. Finally, advantages and disadvantages of using lifting devices in a home setting are presented as a conclusion to the study.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".