Defining and assessing context in healthcare implementation studies: a systematic review
Bibliographic record
Abstract
BACKGROUND: The implementation of evidence-based healthcare interventions is challenging, with a 17-year gap identified between the generation of evidence and its implementation in routine practice. Although contextual factors such as culture and leadership are strong influences for successful implementation, context remains poorly understood, with a lack of consensus regarding how it should be defined and captured within research. This study addresses this issue by providing insight into how context is defined and assessed within healthcare implementation science literature and develops a definition to enable effective measurement of context. METHODS: Medline, PsychInfo, CINAHL and EMBASE were searched. Articles were included if studies were empirical and evaluated context during the implementation of a healthcare initiative. These English language articles were published in the previous 10 years and included a definition and assessment of context. Results were synthesised using a narrative approach. RESULTS: Three thousand and twenty-one search records were obtained of which 64 met the eligibility criteria and were included in the review. Studies used a variety of definitions in terms of the level of detail and explanation provided. Some listed contextual factors (n = 19) while others documented sub-elements of a framework that included context (n = 19). The remaining studies provide a rich definition of general context (n = 11) or aspects of context (n = 15). The Alberta Context Tool was the most frequently used quantitative measure (n = 4), while qualitative papers used a range of frameworks to evaluate context. Mixed methods studies used diverse approaches; some used frameworks to inform the methods chosen while others used quantitative measures to inform qualitative data collection. Most studies (n = 50) applied the chosen measure to all aspects of study design with a majority analysing context at an individual level (n = 29). CONCLUSIONS: This review highlighted inconsistencies in defining and measuring context which emphasised the need to develop an operational definition. By providing this consensus, improvements in implementation processes may result, as a common understanding will help researchers to appropriately account for context in research.
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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 | 0.069 | 0.198 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".