MétaCan
Menu
Back to cohort
Record W4401729338 · doi:10.1111/tct.13797

“Someone to talk to”: A qualitative study of oncology trainees' experience of mentorship around moral distress

2024· article· en· W4401729338 on OpenAlexaffabout
Beatrice Preti, Sarah Wood

Bibliographic record

VenueThe Clinical Teacher · 2024
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsWestern University
Fundersnot available
KeywordsMentorshipDistressQualitative researchPsychologyIntersection (aeronautics)MedicineMedical educationClinical psychologySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Moral distress is an intrinsic part of healthcare, particularly prevalent in oncology practitioners. Previous studies have suggested mentorship may play a role in combatting moral distress; however, there is a lack of good evidence aimed at understanding trainees' experience with either mentorship or moral distress, including the intersection between the two. METHODS: We conducted a single-centre study in the hermeneutic phenomenological approach at a Canadian academic cancer centre. Six semi-structured interviews with senior oncology trainees were conducted and analysed according to the interpretive profiles hermeneutic phenomenological approach. FINDINGS/RESULTS: Key findings include the idea that trainees do find mentorship valuable and helpful in navigating moral distress, which is described as common and inevitable, with a number of triggers and factors identified. However, a mentorship relationship must involve mutual respect, understanding, and honesty in order to be valuable. Additionally, engaging in open, honest discussions with mentors, particularly more senior individuals, is seen as a risk-benefit balance by trainees; vertical mentors bring more wisdom and experience, but may also have a greater impact on a trainee's future. CONCLUSION: This thought-provoking study highlights mentorship as a potential method to combat the troubling phenomenon of moral distress in oncology trainees.

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)0.028
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0280.044
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.537
GPT teacher head0.689
Teacher spread0.152 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Clinical TeacherSame topicEthics in medical practiceFrench-language works237,207