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Record W4400616019 · doi:10.22374/cjmrp.v14i1.79

Assessing“Breaking Bad News” Communication Competency in Midwifery Students

2024· article· en· W4400616019 on OpenAlexaboutno aff
Kathi Wilson

Bibliographic record

VenueCanadian Journal of Midwifery Research and Practice · 2024
Typearticle
Languageen
FieldPsychology
TopicCommunication in Education and Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyObstetricsMedical educationMedicine

Abstract

fetched live from OpenAlex

The communication task of conveying difficult news to patients by health care providers, described in the literature as “breaking bad news,” is one that is often performed poorly. There is, at the same time, a relatively small amount of research in the health sciences education literature devoted to the assessment of this particular competency. Like other health care providers, registered midwives in Canada are expected to have knowledge and skill in this domain of communication. In the Ontario Midwifery Education Program, senior students are expected to develop competence in managing situations involving loss during the childbearing year; however, there is little guidance for preceptors with respect to the assessment of performance for this skill. This article reviews the relevant literature on the assessment of “breaking bad news” and workplace assessment tools in health education, and proposes the use of a clinical encounter card that uses a modified Breaking Bad News Assessment Scale to assist and guide preceptors. Further investigation of the tool’s reliability, validity, and acceptability to preceptors is needed. This article has been peer reviewed.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.002
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 head0.333
GPT teacher head0.604
Teacher spread0.271 · 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
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

Citations1
Published2024
Admission routes1
Has abstractyes

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