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Record W4306157718 · doi:10.3389/fsurg.2022.1009391

MatricS—A novel tool for monitoring professional role development in surgical disciplines

2022· article· en· W4306157718 on OpenAlexaboutno aff
U. Necknig, H. Leyh, Raphaela Waidelich, L. Gernhold, Jan Kiesewetter, Marc Weidenbusch

Bibliographic record

VenueFrontiers in Surgery · 2022
Typearticle
Languageen
FieldPsychology
TopicMentoring and Academic Development
Canadian institutionsnot available
Fundersnot available
KeywordsCompetence (human resources)MedicinePersonalityMedical educationInterpersonal relationshipProfessional developmentInterpersonal communicationPsychologySocial psychology

Abstract

fetched live from OpenAlex

Introduction Mentoring is an effective method for human resource development. Monitoring the process is important for individual mentee/mentor pairs as well as for program directors. Due to individual personality differences of both mentees and mentors and their respective interactions, it is challenging to monitor the individual development process of mentees in a structured manner. This study investigates to what extent a novel instrument, the mentee-based assessment tool for role development of interpersonal competencies in surgical professions (MatricS) can adequately monitor the professional role development process of residents during an established mentoring program. Material and methods In a prospective longitudinal study, the competence development of 31 mentees in two subsequent cohorts was assessed by a modified role matrix based on Canadian Medical Education Directives for Specialists. The evaluation focused on three defined roles (D, developer; N, networker; M, multiplicator) at three levels (private, employer-related, national/international) with four stages of development. For validation of mentee self-assessments, the assessments of the respective mentors were recorded alongside. For correlation analyses, Pearson coefficients were calculated, pre-post-comparisons were done by paired t-tests; significance was assumed at p < 0.05, respectively. Results Mentee self-assessments overall correlated well with the objective mentor assessments (Pearson's r 0.8, p < 0.001). Significant correlations of this magnitude were found for both individual cohorts as well as for all individual roles. The mentees acquired competencies in all roles indicated by significant increases of corresponding MatricS scores. The largest competency gains (mean ± SD) were found in the role D (start: 1.30 ± 0.77, end: 2.13 ± 0.83, p < 0.001). The majority of mentees achieved the prespecified target competency level in >75% of all roles and levels. Conclusion The role development process during mentoring can be reliably monitored by using MatricS. MatricS scores highly correlate between mentees and mentors, indicating that mentee self-assessments are suitable and sufficient for monitoring. These findings help to lessen the work burden on senior surgeons and thus can help to increase the acceptance of mentoring programs in surgical disciplines.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.320
Teacher spread0.285 · 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 designObservational
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".

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Published2022
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