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Piloting virtual mentorship: Evaluating acceptability and influence on wellness and professional identity in medical oncology (MO).

2022· article· en· W4281736048 on OpenAlexaffabout
Andrea S. Fung, Lorelli Nowell, Arfan R. Afzal, Maclean Thiessen, Alexi Campbell, Desirée Hao

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsUniversity of ManitobaCanadian Medical AssociationUniversity of Calgary
Fundersnot available
KeywordsMentorshipMedicineMedical educationDescriptive statisticsProfessional developmentFamily medicine

Abstract

fetched live from OpenAlex

11022 Background: Despite extensive literature on the value of mentorship in academic medicine, little evidence exists to inform mentorship practices in MO. In 2021, the Canadian Association of Medical Oncologists (CAMO) piloted a national, 6-month virtual mentorship program consisting of 2 group mentoring events and additional 1-on-1 mentoring activities, to better support the needs of MO trainees. Feedback was obtained from participants to gain insight into how virtual mentorship might impact physician wellness and formation of professional identity, and to inform future iterations of CAMO’s virtual mentorship strategy. Methods: All Canadian MO residents/fellows were invited to participate in the program. Electronic surveys were completed by participants at baseline, after each group event, and at program completion. Surveys evaluated the program content and format, and used validated questionnaires for evaluating physician wellness/burnout (Stanford Professional Fulfillment Index [SPFI]), and professional identity (Macleod Clark Professional Identity Scale [MCPIS]). Cohort characteristics and survey responses were summarized using descriptive statistics. Procedures for survey data collection and analysis were ethics approved. Results: At baseline, respondents (n = 38) were predominantly female (63%), and < 35 years (76%). 50% were married. On average, 78% of respondents ranked the virtual group mentoring events as meeting expectations. Program strengths identified by participants included meeting mentors outside of their own centre, meeting other trainees from across the country, and learning more about work-life balance/physician wellness. The main critique was insufficient time for interaction. Of the 34 MCPIS respondents, 94% were pleased to belong to the profession of MO, 91% identified positively with members of their profession and 77% felt like a member of the profession themselves. The average score on the SPFI scale was 2.73±0.71 (n = 34). Although 85% of respondents found their work meaningful, 71% satisfying, and 50% felt they were contributing professionally in valued ways, only 38% met criteria for professional fulfilment (score ≥3.00). 27% met criteria for burnout (score ≥1.33), 15% found their work physically exhausting and 9% found their work emotionally exhausting. Conclusions: CAMO’s pilot virtual mentorship program highlights that technology can be successfully leveraged to facilitate mentoring. The majority of MO trainees identified positively with their profession, yet only 38% reached the threshold for professional fulfillment and nearly a third met criteria for burnout. Longitudinal follow-up among mentored trainees is needed to provide insight into whether mentorship may influence physician wellness and professional identity over time.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.105
GPT teacher head0.585
Teacher spread0.480 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

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