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Record W3046084781 · doi:10.1186/s12909-020-02153-x

The “secret sauce” for a mentored training program: qualitative perspectives of trainees in implementation research for cancer control

2020· article· en· W3046084781 on OpenAlex
Rebekah R. Jacob, Angeline Gacad, Christine Pfund, Margaret Padek, David Chambers, Jon Kerner, Anne Sales, Maureen Dobbins, Shiriki Kumanyika, Ross C. Brownson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Medical Education · 2020
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsMcMaster UniversityCanadian Partnership Against Cancer
FundersNational Cancer InstituteWashington University in St. LouisNational Institutes of HealthNational Center for Chronic Disease Prevention and Health PromotionNational Institute of Diabetes and Digestive and Kidney DiseasesDivision of Cancer Prevention, National Cancer InstituteCenters for Disease Control and PreventionU.S. Department of Veterans Affairs
KeywordsCredibilityMedical educationPhoneQualitative researchPsychologyMedicineSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.323
GPT teacher head0.619
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it