Guideline adaptation and implementation planning: a prospective observational study
Bibliographic record
Abstract
BACKGROUND: Adaptation of high-quality practice guidelines for local use has been advanced as an efficient means to improve acceptability and applicability of evidence-informed care. In a pan-Canadian study, we examined how cancer care groups adapted pre-existing guidelines to their unique context and began implementation planning. METHODS: Using a mixed-methods, case-study design, five cases were purposefully sampled from self-identified groups and followed as they used a structured method and resources for guideline adaptation. Cases received the ADAPTE Collaboration toolkit, facilitation, methodological and logistical support, resources and assistance as required. Documentary and primary data collection methods captured individual case experience, including monthly summaries of meeting and field notes, email/telephone correspondence, and project records. Site visits, process audits, interviews, and a final evaluation forum with all cases contributed to a comprehensive account of participant experience. RESULTS: Study cases took 12 to >24 months to complete guideline adaptation. Although participants appreciated the structure, most found the ADAPTE method complex and lacking practical aspects. They needed assistance establishing individual guideline mandate and infrastructure, articulating health questions, executing search strategies, appraising evidence, and achieving consensus. Facilitation was described as a multi-faceted process, a team effort, and an essential ingredient for guideline adaptation. While front-line care providers implicitly identified implementation issues during adaptation, they identified a need to add an explicit implementation planning component. CONCLUSIONS: Guideline adaptation is a positive initial step toward evidence-informed care, but adaptation (vs. 'de novo' development) did not meet expectations for reducing time or resource commitments. Undertaking adaptation is as much about the process (engagement and capacity building) as it is about the product (adapted guideline). To adequately address local concerns, cases found it necessary to also search and appraise primary studies, resulting in hybrid (adaptation plus de novo) guideline development strategies that required advanced methodological skills.Adaptation was found to be an action element in the knowledge translation continuum that required integration of an implementation perspective. Accordingly, the adaptation methodology and resources were reformulated and substantially augmented to provide practical assistance to groups not supported by a dedicated guideline panel and to provide more implementation planning support. The resulting framework is called CAN-IMPLEMENT.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".