A Team-based Approach to Qualitative Inquiry: The Collaborative Retreat
Bibliographic record
Abstract
A team of researchers undertook a collaborative qualitative study to explore beginning dietitians' life experiences and the meaning ascribed to those experiences in the context of dietetic practice. Data were collected using Seidman's three-step in-depth phenomenological interviewing method with 12 beginning dietitians who were graduates of the three participating dietetic programs. We outline the collaborative research process and highlight a writing and data analysis technique described as the collaborative retreat, a face-to-face, two-day gathering that facilitated the researchers' collective decision-making and organization, discussion, and analysis of this complex qualitative data set. Use of a listening guide aided researchers' understanding and interpretation of participant voices. Researchers concluded that the overall collaborative qualitative research process was positive and self-fulfilling, and that it resulted in multiple benefits for them individually and the research project collectively. Researchers were able to work through methodological and theoretical issues as these arose, with the assistance of technology, writing, listening, and dialogue. Relationship building and relationship maintenance emerged as factors critical to the success of the research process. Collaborative research teams that are committed to listening, writing, and dialogue will find that the collaborative retreat can be a productive site of knowledge generation and mentorship.
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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 | 0.106 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.004 | 0.005 |
| 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".