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Record W2154847324 · doi:10.1002/crq.211

What sticks: How medical residents and academic health care faculty transfer conflict resolution training from the workshop to the workplace

2008· article· en· W2154847324 on OpenAlexafffund
Ellen B. Zweibel, Rose Goldstein, John A. Manwaring, Meridith B. Marks

Bibliographic record

VenueConflict Resolution Quarterly · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsMcGill UniversityUniversity of Ottawa
FundersRoyal College of Physicians and Surgeons of CanadaUniversity of Ottawa
KeywordsConflict resolutionMultidisciplinary approachPerspective (graphical)Medical educationHealth carePsychologyPatient careQualitative researchConflict managementNursingMedicinePolitical scienceSociologyComputer science

Abstract

fetched live from OpenAlex

Abstract Workshops in conflict resolution were given to enhance the ability of residents and academic health care faculty to collaborate in multidisciplinary teams, patient care, hospital committees, public health issues, teaching, and research. A qualitative research study on the transfer of learning from the workshops to the workplace reports on the attitude, knowledge, and skills consistently reported both immediately after the workshops and twelve months later. Learners' descriptions of workplace conflict confirmed they gained a positive outlook on conflict and their own ability to solve problems and apply conflict resolution skills, such as interest analysis and communication techniques, to gain perspective, reduce tension, increase mutual understanding, and build relationships in patient care, teaching, research, and administration.

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)0.019
metaresearch head score (Gemma)0.078
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.078
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0150.005
Scholarly communication0.0080.007
Open science0.0040.006
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.002

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 head0.122
GPT teacher head0.365
Teacher spread0.243 · 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".

Quick stats

Citations3
Published2008
Admission routes2
Has abstractyes

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