Exploring the role of regulation and the care of older people with depression living in long-term care? A systematic scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: This systematic scoping review will explore the role of regulation on the care of older people living with depression in long-term care. Depression presents a significant burden to older people living in long-term care. Regulation in the long-term care sector has increased, but there are still concerns about quality of care in the sector. METHODS AND ANALYSIS: Using Arksey and O'Malley's scoping review methodology as a guide, our scoping review will search several databases: Embase; MEDLINE (using the OVID platform); Psych info; Ageline; and CINAHL, alongside the grey literature. An expert librarian has assisted the research team, using the Peer Review of Electronic Search Strategies, to assess the search strategy. The research team has formulated search strategies and two reviewers will independently screen studies for final study selection. We will summarise extracted data in tabular format; use a narrative format to describe their relevance; and finally, identify knowledge gaps and topics for future research. ETHICS AND DISSEMINATION: This scoping review will outline the scope of the existing literature related to the influence of regulation on the care of older people living with depression in long-term care. The scoping review findings will be disseminated through publication in a peer-reviewed journal. The findings will be useful to policy-makers, managers and clinicians working in the long-term care sector.
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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 | 0.166 | 0.145 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.045 | 0.012 |
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".