Decision Makers' Allocation of Home-Care Therapy Services: A Process Map
Bibliographic record
Abstract
PURPOSE: To explore decision-making processes currently used in allocating occupational and physical therapy services in home care for complex long-stay clients in Ontario. METHOD: An exploratory study using key-informant interviews and client vignettes was conducted with home-care decision makers (case managers and directors) from four home-care regions in Ontario. The interview data were analyzed using the framework analysis method. RESULTS: The decision-making process for allocating therapy services has four stages: intake, assessment, referral to service provider, and reassessment. There are variations in the management processes deployed at each stage. The major variation is in the process of determining the volume of therapy services across home-care regions, primarily as a result of financial constraints affecting the home-care programme. Government funding methods and methods of information sharing also significantly affect home-care therapy allocation. CONCLUSION: Financial constraints in home care are the primary contextual factor affecting allocation of therapy services across home-care regions. Given the inflation of health care costs, new models of funding and service delivery need to be developed to ensure that the right person receives the right care before deteriorating and requiring more costly long-term care.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".