Addressing the Long-Term Care Crisis: Identifying Opportunities for Improvement Using Rapid Reviews
Bibliographic record
Abstract
Background: Long-term care (LTC) facilities require urgent, evidence-based care renewal. During 2020 three medical student-driven research projects aiming to study care satisfaction, patient care team dynamics, and advance care directive effectiveness in a local LTC facility required a marked shift in approach due to COVID-19 regulations. Methods: All three projects were re-invented as rapid reviews from their initial designs intended to provide a baseline for quality improvement projects. English-limited PubMed searches for publications within the past 10 years were undertaken. Review articles were prioritized and supplemented by individual studies. Students reviewed the initial abstracts, reviewed them with a supervisor/mentor, assessed the articles for quality, and synthesized major themes. Results: A total of 52 publications were evaluated for the final synthesis of all three projects. Relevant information was retrieved for all three areas, suitable for local evaluation/intervention at micro, meso, and macro policy levels. Conclusions: Rapid reviews of issue-specific, long-term care literature are low resource avenues towards coordinated care improvement. They may also serve as rapid means for regular policy updates while providing next-generation care providers with improved LTC perspectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.110 | 0.237 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.024 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".