Measurement of integrated healthcare delivery: a systematic review of methods and future research directions
Bibliographic record
Abstract
BACKGROUND: Integrated healthcare delivery is a policy goal of healthcare systems. There is no consensus on how to measure the concept, which makes it difficult to monitor progress. PURPOSE: To identify the different types of methods used to measure integrated healthcare delivery with emphasis on structural, cultural and process aspects. METHODS: Medline/Pubmed, EMBASE, Web of Science, Cochrane Library, WHOLIS, and conventional internet search engines were systematically searched for methods to measure integrated healthcare delivery (published - April 2008). RESULTS: Twenty-four published scientific papers and documents met the inclusion criteria. In the 24 references we identified 24 different measurement methods; however, 5 methods shared theoretical framework. The methods can be categorized according to type of data source: a) questionnaire survey data, b) automated register data, or c) mixed data sources. The variety of concepts measured reflects the significant conceptual diversity within the field, and most methods lack information regarding validity and reliability. CONCLUSION: Several methods have been developed to measure integrated healthcare delivery; 24 methods are available and some are highly developed. The objective governs the method best used. Criteria for sound measures are suggested and further developments should be based on an explicit conceptual framework and focus on simplifying and validating existing methods.
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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 | 0.096 | 0.187 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.007 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".