Long COVID in long-term care: a rapid realist review
Bibliographic record
Abstract
OBJECTIVES: The goals of this rapid realist review were to ask: (a) what are the key mechanisms that drive successful interventions for long COVID in long-term care (LTC) and (b) what are the critical contexts that determine whether the mechanisms produce the intended outcomes? DESIGN: Rapid realist review. DATA SOURCES: Medline, CINAHL, Embase, PsycINFO and Web of Science for peer-reviewed literature and Google for grey literature were searched up to 23 February 2023. ELIGIBILITY CRITERIA: We included sources focused on interventions, persons in LTC, long COVID or post-acute phase at least 4 weeks following initial COVID-19 infection and ones that had a connection with source materials. DATA EXTRACTION AND SYNTHESIS: Three independent reviewers searched, screened and coded studies. Two independent moderators resolved conflicts. A data extraction tool organised relevant data into context-mechanism-outcome configurations using realist methodology. Twenty-one sources provided 51 intervention data excerpts used to develop our programme theory. Synthesised findings were presented to a reference group and expert panel for confirmatory purposes. RESULTS: Fifteen peer-reviewed articles and six grey literature sources were eligible for inclusion. Eleven context-mechanism-outcome configurations identify those contextual factors and underlying mechanisms associated with desired outcomes, such as clinical care processes and policies that ensure timely access to requisite resources for quality care delivery, and resident-centred assessments and care planning to address resident preferences and needs. The underlying mechanisms associated with enhanced outcomes for LTC long COVID survivors were: awareness, accountability, vigilance and empathetic listening. CONCLUSIONS: Although the LTC sector struggles with organisational capacity issues, they should be aware that comprehensively assessing and monitoring COVID-19 survivors and providing timely interventions to those with long COVID is imperative. This is due to the greater care needs of residents with long COVID, and coordinated efficient care is required to optimise their quality of life.
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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 | 0.175 | 0.443 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.019 | 0.021 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".