Use of theory to plan or evaluate guideline implementation among physicians: a scoping review
Bibliographic record
Abstract
BACKGROUND: Guidelines support health care decision-making and high quality care and outcomes. However, their implementation is sub-optimal. Theory-informed, tailored implementation is associated with guideline use. Few guideline implementation studies published up to 1998 employed theory. This study aimed to describe if and how theory is now used to plan or evaluate guideline implementation among physicians. METHODS: A scoping review was conducted. MEDLINE, EMBASE, and The Cochrane Library were searched from 2006 to April 2016. English language studies that planned or evaluated guideline implementation targeted to physicians based on explicitly named theory were eligible. Screening and data extraction were done in duplicate. Study characteristics and details about theory use were analyzed. RESULTS: A total of 1244 published reports were identified, 891 were unique, and 716 were excluded based on title and abstract. Among 175 full-text articles, 89 planned or evaluated guideline implementation targeted to physicians; 42 (47.2%) were based on theory and included. The number of studies using theory increased yearly and represented a wide array of countries, guideline topics and types of physicians. The Theory of Planned Behavior (38.1%) and the Theoretical Domains Framework (23.8%) were used most frequently. Many studies rationalized choice of theory (83.3%), most often by stating that the theory described implementation or its determinants, but most failed to explicitly link barriers with theoretical constructs. The majority of studies used theory to inform surveys or interviews that identified barriers of guideline use as a preliminary step in implementation planning (76.2%). All studies that evaluated interventions reported positive impact on reported physician or patient outcomes. CONCLUSIONS: While the use of theory to design or evaluate interventions appears to be increasing over time, this review found that one half of guideline implementation studies were based on theory and many of those provided scant details about how theory was used. This limits interpretation and replication of those interventions, and seems to result in multifaceted interventions, which may not be feasible outside of scientific investigation. Further research is needed to better understand how to employ theory in guideline implementation planning or evaluation.
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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 | 0.097 | 0.271 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.037 | 0.026 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".