Waiting list management practices for home-care occupational therapy in the province of Quebec, Canada
Bibliographic record
Abstract
Referral prioritisation is commonly used in home-based occupational therapy to minimise the negative impacts of waiting, but this practice is not standardised. This may lead to inequities in access to care, especially for clients considered as low priority, who tend to bear the brunt of lengthy waiting lists. This cross-sectional study aimed to describe waiting list management practices targeting low-priority clients in home-based occupational therapy in the province of Quebec, Canada, and to investigate the association between these practices and the length of the waiting list. A structured telephone interview was conducted in 2012-2013 with the person who manages the occupational therapy waiting list in 55 home care programmes across Quebec. Questions pertained to strategies aimed at servicing low-priority clients, the date of the oldest referral and the number of clients waiting. Results were analysed using descriptive statistics and non-parametric tests. The median wait time for the oldest referral was 18 months (range: 2-108 months). A variety of strategies were used to service low-priority clients. Programmes that used no strategies to service low-priority clients (n = 16) had longer wait times (P < 0.0001) and a greater number of people on the waiting list (P = 0.006) compared with programmes that applied a maximum wait time target (n = 12). In conclusion, diverse strategies exist to allocate services to low-priority clients in home-based occupational therapy programmes. However, in programmes where none of these strategies are used, low-priority clients may be denied access to services indefinitely.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".