Lessons learned from undertaking community-based participatory research dissertations: the trials and triumphs of two junior health scholars.
Bibliographic record
Abstract
BACKGROUND: For graduate students addressing health issues pertinent to marginalized communities, community-based participatory research (CBPR) methods may be an appropriate mode of inquiry. Although there are a number of useful guides on conducting traditional doctoral dissertations (TDD), there is a paucity of similar resources for students engaged in CBPR. OBJECTIVES: Drawing on our own experiences, we aimed to describe the key lessons learned from doing participatory doctoral research. Furthermore, this paper outlines 6 suggestions for those who may be considering or already conducting a CBPR dissertation. Suggestions are derived from elements of the CBPR process that were employed in our own projects. LESSONS LEARNED: Upon reflection on our experiences conducting CBPR dissertations, we identified 4 lessons learned: (1) to understand the differences between TDDs and the CBPR approach; (2) to be aware of and able to clearly articulate the advantages of CBPR doctoral dissertations; (3) to acknowledge and plan for the possible challenges of CBPR doctoral research; and (4) to recognize aspects of the CBPR process that contribute to the successful completion of doctoral projects. CONCLUSION: This paper provides an additional resource for doctoral students, based on our own experiences working on CBPR projects. Despite many of the obstacles and challenges, we found the process of engaging in CBPR dissertations deeply rewarding, and hope that our experiences are useful to others.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.220 | 0.309 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.031 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.006 | 0.037 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".