How Much Allied Health Therapy Care Is Enough? An Evidence Scan
Bibliographic record
Abstract
Purpose: Pressure to eliminate low-value health care is increasing internationally. This pressure has produced an urgent need to identify evidence-based methods to determine the value of allied health (AH) care, particularly to recognize when additional AH care adds no further benefits. This article reports on the published methods of determining the value of AH care. Method: We systematically scanned PubMed, MEDLINE, AMED, CINAHL, PsycINFO, and the Grey Literature Review database from inception until July 2018 for peer-reviewed English-language literature. Hierarchy of evidence and information on study design and the methods or measures used to determine the value of AH care were extracted. Results: Of 189 articles, 30 were potentially relevant; after the full text was read, all were included. Of these, 24 reported on ways of determining the value of AH care, and 6 described the optimal provision of AH episodes of care. No methods were reported that could be applied to establish when enough AH therapy had been provided. Conclusion: This review found a variety of attributes of value in AH care, but no standard value measure or methods to determine what constituted enough AH care. Repeated measurement of the standard attributes of value and costs is required throughout episodes of AH care to better understand the impact of AH care from the different stakeholders’ 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 | 0.034 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".