Patient contracts for chronic medical conditions
Bibliographic record
Abstract
OBJECTIVE: To describe how and why patient contracts are used for the management of chronic medical conditions. DATA SOURCES: A scoping review was conducted in the following databases: MEDLINE, Embase, AMED, PsycInfo, Cochrane Library, CINAHL, and Nursing & Allied Health. Literature from 1997 to 2017 was included. STUDY SELECTION: Articles were included if they were written in English and described the implementation of a patient contract by a health care provider for the management of a chronic condition. Articles had to present an outcome as a result of using the contract or an intervention that included the contract. SYNTHESIS: Of the 7528 articles found in the original search, 76 met the inclusion criteria for the final review. Multiple study types were included. Extensive variety in contract elements, target populations, clinical settings, and cointerventions was found. Purposes for initiating contracts included behaviour change and skill development, including goal development and problem solving; altering beliefs and knowledge, including motivation and perceived self-efficacy; improving interpersonal relationships and role clarification; improving quality and process of chronic care; and altering objective and subjective health indices. How contracts were developed, implemented, and assessed was inconsistently described. CONCLUSION: More research is required to determine whether the use of contracts is accomplishing their intended purposes. Questions remain regarding their rationale, development, and implementation.
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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 | 0.013 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".