Uncovering the wisdom hidden between the lines: the Collaborative Reflexive Deliberative Approach
Bibliographic record
Abstract
Background: Meta-analysis and meta-synthesis have been developed to synthesize results across published studies; however, they are still largely grounded in what is already published, missing the tacit 'between the lines' knowledge generated during many research projects that are not intrinsic to the main objectives of studies. Objective: To develop a novel approach to expand and deepen meta-syntheses using researchers' experience, tacit knowledge and relevant unpublished materials. Methods: We established new collaborations among primary health care researchers from different contexts based on common interests in reforming primary care service delivery and a diversity of perspectives. Over 2 years, the team met face-to-face and via tele- and video-conferences to employ the Collaborative Reflexive Deliberative Approach (CRDA) to discuss and reflect on published and unpublished results from participants' studies to identify new patterns and insights. Results: CRDA focuses on uncovering critical insights, interpretations hidden within multiple research contexts. For the process to work, careful attention must be paid to ensure sufficient diversity among participants while also having people who are able to collaborate effectively. Ensuring there are enough studies for contextual variation also matters. It is necessary to balance rigorous facilitation techniques with the creation of safe space for diverse contributions. Conclusions: The CRDA requires large commitments of investigator time, the expense of convening facilitated retreats, considerable coordination, and strong leadership. The process creates an environment where interactions among diverse participants can illuminate hidden information within the contexts of studies, effectively enhancing theory development and generating new research questions and strategies.
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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 | 0.688 | 0.657 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.030 | 0.013 |
| Science and technology studies | 0.014 | 0.080 |
| Scholarly communication | 0.040 | 0.028 |
| Open science | 0.020 | 0.039 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".