Appropriateness of healthcare interventions: Concepts and scoping of the published literature
Bibliographic record
Abstract
OBJECTIVES: This report is a scoping review of the literature with the objective of identifying definitions, conceptual models and frameworks, as well as the methods and range of perspectives, for determining appropriateness in the context of healthcare delivery. METHODS: To lay groundwork for future, intervention-specific research on appropriateness, this work was carried out as a scoping review of published literature since 1966. Two reviewers, with two screens using inclusion/exclusion criteria based on the objective, focused the research and articles chosen for review. RESULTS: The first screen examined 2,829 abstracts/titles, with the second screen examining 124 full articles, leaving 37 articles deemed highly relevant for data extraction and interpretation. Appropriateness is defined largely in terms of net clinical benefit to the average patient and varies by service and setting. The most widely used method to assess appropriateness of healthcare services is the RAND/UCLA Model. There are many related concepts such as medical necessity and small-areas variation. CONCLUSIONS: A broader approach to determining appropriateness for healthcare interventions is possible and would involve clinical, patient and societal 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 | 0.312 | 0.487 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.059 | 0.046 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".