The “secret sauce” for a mentored training program: qualitative perspectives of trainees in implementation research for cancer control
Bibliographic record
Abstract
BACKGROUND: Mentored training approaches help build capacity for research through mentoring networks and skill building activities. Capacity for dissemination and implementation (D&I) research in cancer is needed and mentored training programs have been developed. Evaluation of mentored training programs through quantitative approaches often provides us with information on "what" improved for participants. Qualitative approaches provide a deeper understanding of "how" programs work best. METHODS: Qualitative interviews were conducted with 21 fellows of the National Cancer Institute-funded Mentored Training for Dissemination and Implementation in Cancer to gain understanding of their experiences with mentoring received during the program. Fellows were selected from all 55 trained participants based upon their gain in D&I research skills (highest and lowest) and number of collaborative connections in the program network (highest and lowest) reported in previous quantitative surveys. Phone interviews were recorded with permission, transcribed verbatim, and de-identified for analysis. Codes were developed a priori to reflect interview guide concepts followed by further development and iterative coding of three common themes that emerged: 1) program and mentoring structure, 2) importance of mentor attributes, and 3) enhanced capacity: credentials, confidence, credibility and connections. RESULTS: Interviews provided valuable information about program components that worked best and impacts attributed to participation in the program. Fellows reported that regular monthly check-in calls with mentors helped to keep their research moving forward and that group mentoring structures aided in their learning of basic D&I research concepts and their application. Accessible, responsive, and knowledgeable mentors were commonly mentioned by fellows as a key to their success in the program. Fellows mentioned various forms of impact that they attributed to their participation in the program including gaining credibility in the field, a network of peers and experts, and career developments (e.g., collaborative publications and grant funding). CONCLUSIONS: These findings suggest that mentored training works best when mentoring is structured and coupled with applied learning and when respected and dedicated mentors are on board. Increased scientific collaborations and credibility within a recognized network are important trainee experiences that should be considered when designing, implementing, and sustaining mentored training programs.
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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 | 0.075 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.017 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| 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".