The role of regulation in the care of older people with depression living in long-term care: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: This aim of this study was to explore the role of regulation on the quality of care of older people living with depression in LTC, which in this paper is a domestic environment providing 24-h care for people with complex health needs and increased vulnerability. METHODS: We conducted a systematic scoping review. A peer reviewed search strategy was developed in consultation with a specialist librarian. Several databases were searched to identify relevant studies including: Embase (using the OVID platform); MEDLINE (using the OVID platform); Psych info (using the OVID platform); Ageline (using the EBSCO platform); and CINHAL (using the EBSCO platform). Articles were screened by three reviewers with conflicts resolved in consultation with authors. Data charting was completed by one reviewer, with a quality check performed by a second reviewer. Key themes were then derived from the included studies. RESULTS: The search yielded 778 unique articles, of which 20 were included. Articles were grouped by themes: regulatory requirements, funding issues, and organizational issues. CONCLUSION: The highly regulated environment of LTC poses significant challenges which can influence the quality of care of residents with depression. Despite existing evidence around prevalence and improved treatment regimens, regulation appears to have failed to capture the best practice and contemporary knowledge available. This scoping review has identified a need for further empirical research to explore these issues.
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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 | 0.051 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".