Facilitators and Barriers to Successful Collaboration in Guideline Development: Experiential Insights of Collaborative Guideline Developers
Bibliographic record
Abstract
ABSTRACT Objective The current study aimed to elucidate themes that contributed to successful and unsuccessful collaborative guideline development between organisations. Study Design and Setting The reporting of qualitative methods was checked against the Consolidated criteria for reporting qualitative research (COREQ) checklist. A purposive sampling approach was used to recruit participants with experience or affiliations in collaborative guideline development. Participants completed a pre‐interview questionnaire and took part in semi‐structured interviews. Interview transcripts were coded using inductive thematic analysis methodology. Results The thematic analysis of guideline developer interviews ( n = 14) identified three interconnected theme sets and secondary sub‐themes. Misalignment of collaboration purpose and scope, misalignment of guideline development methodologies, and perception that such collaborations require more time and effort were identified as barriers. Technologies and templates that support collaborative work, initial scoping and planning among collaborators, organisational guideline topic prioritisation work, culture of collaboration, and recognising collaboration benefits were identified as facilitators. Cross‐cutting themes, which appeared to either enhance or hinder collaboration based on interviewee context, included past collaborative experiences, publication of completed work, and levels of knowledge and training among collaborators. Conclusion This analysis demonstrated facilitators and barriers to collaborative guideline development are interconnected. Future research into collaborative guideline development should look towards validating these findings in a more geographically diverse sample and investigate the mechanisms through which barrier, facilitator and cross‐cutting themes contribute to guideline collaboration between organisations. Further understanding of factors that can impede or expedite successful collaboration provide opportunities to identify effective strategies for overcoming perceived barriers to collaboration. Findings from this study can be used to bolster collaborative efforts by encouraging alignment in processes, methodology, and expectations, as well as promoting collective knowledge and resource sharing between collaborators within different guideline development organisations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".