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Record W4385668859 · doi:10.2196/48263

Benefits of Mentoring in Oncology Education for Mentors and Mentees: Pre-Post Interventional Study of the British Oncology Network for Undergraduate Societies' National Oncology Mentorship Scheme

2023· article· en· W4385668859 on OpenAlexvenueno aff
Taylor Fulton-Ward, Robert Bain, Emma G Khoury, Sumirat M. Keshwara, Prince Josiah Joseph, Peter J. Selby, Christopher P. Millward

Bibliographic record

VenueJMIR Medical Education · 2023
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
FundersCancer Research UK
KeywordsMentorshipOncologyInternal medicineSpecialtyMedical educationCurriculumMedicinePsychologyFamily medicinePedagogy

Abstract

fetched live from OpenAlex

BACKGROUND: Formal education of oncology is lacking in many undergraduate medical curricula. Mentoring schemes can expose participants to specific areas of medicine and may address the shortfalls in oncology education. Few mentoring schemes have been designed within the United Kingdom, especially within oncology. There is a need to understand reasons for mentor and mentee participation in such schemes and to identify ways to minimize barriers to engagement. OBJECTIVE: This study identifies motivations for participation in an oncology mentoring scheme and its benefits and limitations to both the mentee and the mentor. METHODS: The British Oncology Network for Undergraduate Societies launched a National Oncology Mentorship Scheme (NOMS) on September 1, 2021. Mentees (medical student or foundation doctor) were paired with mentors (specialty registrar or consultant), for 6 months of mentoring. In total, 86 mentors and 112 mentees were recruited to the scheme. The mentees and mentors were asked to meet at least 3 times during this period and suggestions were provided on the content of mentoring. Mentees and mentors were invited to complete a prescheme questionnaire, exploring motivations for involvement in the scheme, current experiences within oncology, and knowledge and interests in the field. At the end of the scheme, mentors and mentees were asked to complete a postscheme questionnaire exploring experiences and benefits or limitations of participation. Paired analysis was performed using the Wilcoxon signed-rank test. For free text data, content analysis was applied to summarize the main themes in the data. RESULTS: Of the 66 (59%) mentees who completed the prescheme questionnaire, 41 (62%) were clinical, 21 (32%) preclinical medical students, and the remainder were junior doctors. For mentees, networking was the primary reason for joining the scheme (n=25, 38%). Mentees ranked experience of oncology at medical school at 3 on 10 (IQR 2-5). In this, 46 (53%) mentors completed the prescheme questionnaire, 35 (76%) were registrar level, and the remainder were consultant level (n=11). The most common reason for mentor participation was to increase awareness and interest in the field (n=29, 63%). Of those who completed the prescheme questionnaire, 23 (35%) mentees and 25 (54%) mentors completed the postscheme questionnaire. Knowledge in all areas of oncology assessed significantly increased during the scheme (P<.001). Most mentees (n=21, 91%) and mentors (n=18, 72%) felt they had benefited from the scheme. Mentees cited gaining insights into oncology as most beneficial; and mentors, opportunities to develop professionally. Whilst mentees did not report any barriers to participating in the scheme, mentors stated lack of time as the greatest barrier to mentoring. CONCLUSIONS: British Oncology Network for Undergraduate Societies' NOMS is expanding and is beneficial for mentees through increasing knowledge, providing exposure, and career advice in oncology. Mentors benefit from improving their mentoring skills and personal satisfaction.

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 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.047
GPT teacher head0.428
Teacher spread0.382 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2023
Admission routes1
Has abstractyes

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