ISQUA17-1632QUALITY IN LONG-TERM CARE: AN EXPANDED VIEW
Bibliographic record
Abstract
Regulation and accreditation drive the quality agenda in long-term care (LTC), and are associated with compliance with legislation. Traditional models of regulation and accreditation in LTC are deterrence based, and are ineffective in improving quality; time consuming; expensive; and onerous for smaller facilities. ‘New Governance’, is a tri-partisan approach to quality, which is offered in the literature as a means to involve interested parties, traditionally excluded, in the quality agenda in LTC. The approach is characterised by participation; flexibility; responsiveness; dynamic learning; and self-enforced regulation. However, ‘New Governance’, rather than being a means to improve the interdependence between legislation and enforcement, to facilitate a more dynamic approach, has been critiqued. It is perceived, by some, as a means to de-regulate the LTC sector. An expanded approach is needed which not only embraces the need for compliance with legislation, but is still collaborative and values the perspectives of different stakeholders. This work argues that traditional mechanisms for achieving quality in LTC do not account for person- and family-centeredness or contextual factors.
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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 | 0.060 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.031 | 0.013 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.037 | 0.024 |
| Insufficient payload (model declined to judge) | 0.069 | 0.017 |
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".