Reflecting on reflections: Critically apprising the benefits of member reflections for researchers, participants, findings, and studies
Bibliographic record
Abstract
Research rigour is the alignment of methodology with philosophical paradigms, while maintaining precision and cohesiveness of ideas; it is a mark of research excellence. Adding rigor to qualitative research can occur through member reflections, which offers an opportunity to clarify ideas, explore gaps in initial findings, and generate additional data with participants. The purpose of this presentation is to critically appraise the benefits of member reflections to researchers, participants/knowledge-users, study findings, and overall research program. Using our own research as an exemplar, member reflections were employed in developing a user-informed and athlete-tailored self-compassion program for women athletes. In our study, member reflections occurred through multiple phases of focus groups. Engaging in multiple phases of data generation meant the researchers spent more time immersed in the data and conversing with participants, which led to many initially unexplored questions, insights to which offered depth and richness to the final findings. Follow-up focus groups allowed the researchers to lean into those questions to explore gaps in initial findings and clarify ideas from initial data generation. Member reflections also offered an opportunity to more fully co-produce findings with knowledge users, who were women athletes in our study. These additional insights served to amplify participant voices (which may otherwise go unheard), and generate meticulous, robust, and enriched findings to inform future phases of our larger research project. Member reflections create an opportunity for researchers to gain clarity and depth, as findings are co-produced with participants, which generates rigorous research to inform future studies.
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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 | 0.262 | 0.544 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.023 | 0.049 |
| Scholarly communication | 0.040 | 0.028 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.012 | 0.029 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".