UPDATE: Consistency of Home Care Personnel Under Managed Competition: A Case Study from Ontario (Shortened Version Presented at the Knowledge to Wisdom Conference)
Bibliographic record
Abstract
Measuring Consistency of Personnel in Home care: Current Challenges and Findings Consistency of personnel is important to ensuring continuity of care for home care clients. It is particularly important to those clients who are at high risk for adverse effects when provider changes occur. Information about the extent to which clients experience consistency of personnel is difficult to collect in Ontario. It resides with individual provider agencies rather than with the Community Care Access Centres (CCACs) that arrange service delivery. We will present findings from the Continuity of Care in Home Care study which obtained information directly from service provider agencies on the number of providers that 500 CCAC clients saw. These clients received either nursing or homemaking services or both. Factors linked with the mean number of providers experienced by a client and with the total number of providers experienced by a client during up to a year of service delivery will be highlighted. Factors affecting the frequency of provider changes will be discussed along with the implications of our findings for clients, service providers, agencies and policy makers.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".