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Record W2785806727 · doi:10.4300/jgme-d-17-00728.1

Redirecting the Focus of Resident Mentorship

2018· letter· en· W2785806727 on OpenAlexaboutno aff
Benjamin Persons, Michael J. Agatstein, Ju Hee Kim

Bibliographic record

VenueJournal of Graduate Medical Education · 2018
Typeletter
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
Fundersnot available
KeywordsMentorshipFocus (optics)Medical educationMedicineData scienceComputer science

Abstract

fetched live from OpenAlex

The idea of milestones for resident mentorship was introduced by Khan and colleagues1 in a recent article in the Journal of Graduate Medical Education. We take for granted the authors' assertion that mentorship is an essential component that helps people navigate the challenges inherent in medical training. Part of the appeal of the graduate medical education process is the close relationship between resident and attending physician: a type of apprenticeship that once existed across many professions, which has increasingly been lost in the transition to modernity. At its best, this relationship extends beyond the formal bounds of patient care to encouraging and supporting a resident's career goals and addressing his or her individual concerns. The first year of residency requires the physician in training to work with a variety of attending physicians. Such an arrangement allows the resident to select the faculty member who seems to be a good fit for guidance and support going forward.We appreciate the efforts of the Accreditation Council for Graduate Medical Education Council of Review Committee Residents in compiling a number of specified mentor competencies.1 While these are broad enough to be applicable to different residency programs, we feel that interpersonal compatibility between mentor and mentee should take precedence. The effectiveness of mentorship appears to increase when mentor and mentee are matched according to personality type.2 This is even more important when, as Khan et al suggested,1 mentors are selected among residents who are more advanced in their training but may lack the authority attached to the title of attending physician. A “speed-dating” strategy to pair junior residents with senior residents was described by Caine et al.3 Residents ranked 3 preferred resident mentors after meeting individually with each of the mentoring candidates one-on-one for 90 seconds. This increased resident satisfaction to 85%, compared with 30% when mentors were assigned by the program. A similar process has been endorsed by the otolaryngology program at the University of Alberta,4 where residents are directed to select from a pool of prospective faculty mentors based on deidentified data featuring their character traits and expertise. The program found statistically significant decreases in depersonalization and emotional exhaustion when residents had a role in selecting mentors based on compatible traits.4The literature cited here is only a small sample from the multitude of sources addressing mentorship as a means to combat resident burnout. The individualization of the mentoring process appears to be beneficial. Peer and resident-faculty mentorship, once left to the residents to pursue, are increasingly recognized by programs for their ability to offset the challenges faced during residency.We believe the best way to establish these relationships is to facilitate encounters between like-minded individuals. Defining mentoring milestones, while important, should not be used to rigidly demarcate the relationship between mentor and mentee. In the short amount of time accorded by the rigors of training, personal compatibility should be the foremost consideration in this relationship.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.316
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
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 science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.080
GPT teacher head0.386
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreCommentary

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
Published2018
Admission routes1
Has abstractyes

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