Building consensus on Winnipeg's personal care home paneling criteria
Bibliographic record
Abstract
Background: It is important to ensure that the right criteria are used to admit (panel) older Manitobans into personal care homes (PCHs), so that only people who must exclusively be cared for in this setting are admitted, while all others remain supported in the community. However, research shows that 10.4% of people admitted into Winnipeg PCHs are less clinically burdened, and that Manitoba has the second highest supply of PCH beds per capita age 85 and older. This study examines the kinds of need factors (e.g., cognitive impairment), by their severity level, that community representatives believe should be used to admit people into a Winnipeg PCH (unconditionally or pending the types of community supports available). Methods: Guided by the Anderson-Newman Behavioural Model of Health Services Utilization, a Delphi survey method was utilized to determine how need factors (both physical and psychosocial), by their severity level, should be used to make PCH admission decisions (i.e., independently or pending available community supports). The research was conducted in three sequential phases. A literature review was conducted in phase 1 to gather information to be used in the Delphi survey. Phase 2 involved creating and piloting the survey. Phase 3 involved applying the Delphi survey to a group of community representatives with experience as an informal caregiver for someone during a PCH paneling process in Winnipeg. Results: With one exception (i.e., someone who has severe cognitive impairment or has been diagnosed with dementia/Alzheimer’s), community representatives did not agree that people with severe need challenges should be admitted to a PCH unconditionally. Participants most commonly agreed on scenarios where people should almost never be admitted to a PCH, or where this admission depends on the kinds of supports available in the community. Conclusion: With one exception, across multiple factors and severity levels, community representatives report that PCH admission decisions should consider need factors combined with the kinds of community support available, rather than need factors alone. These findings have implications on the kinds of community-based supports that should be offered in Winnipeg to prevent or delay admission to a PCH.
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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 | 0.396 | 0.361 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.012 | 0.030 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".