A realist review of the home care literature and its blind spots
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: There is a large body of literature from all over the world that describes, analyzes, or evaluates home care models and interventions. The present article aims to identify the practical lessons that can be gained from a systematic examination of that literature. METHOD: We conducted a three-step sequential search process from which 113 documents were selected. That corpus was then narratively analysed according to a realist review approach. RESULTS: A first level of observation is that there are multiple blind spots in the existing literature on home care. The definition and delimitation of what constitutes home care services is generally under-discussed. In the same way, the composition of the basket of care provided and its fit with the need of recipients is under-addressed. Finally, the literature relies heavily on RCTs whose practical contribution to decisions or policy is disputable. At a second level, our analysis suggests that three mechanisms (system integration, case management and relational continuity) are core characteristics of home care models' effectiveness. CONCLUSION: We conclude by providing advice for supporting the design and implementation of stronger home care delivery systems. Our analysis suggests that doing so implies a series of sequential steps: identify what system-level goals the model should achieve and which populations it should serve; identify what type of services are likely to achieve those goals in order to establish a basket of services; and finally, identify the best ways and specific means to effectively and efficiently provide those services. Those same steps can also support ex-post evaluations of existing home care systems.
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 | 0.125 | 0.267 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.023 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".