What We Learned about Mentoring Research Assistants Employed in a Complex, Mixed-Methods Health Study
Bibliographic record
Abstract
We investigated the experiences of research assistants in their dual role as both employees and trainees, when they were employed in a complex, mixedmethods, Canadian study on the everyday experience of living with and managing a chronic condition. A total of 13 research assistants participated in one or more components of this study: a survey (n = 11), focus group interview (n = 7), and/or individual interview (n = 13). Thematic analysis identified two key themes: what faculty mentors should provide to research assistants before they begin their work, and what faculty mentors need to know in order to effectively offer ongoing support to research assistants. Our results provide valuable insights for new and experienced faculty members who employ research assistants and for research assistants employed in funded research projects. Our results can inform the development of regulations to ensure that research assistants have greater protection as both trainees and employees.
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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 | 0.161 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".