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Record W7161974863 · doi:10.82308/13846

Measuring emotions in medical students: validation of the Japanese version of the medical emotion scale

2019· dissertation· en· W7161974863 on OpenAlexaboutno aff
Osamu Nomura

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsnot available
Fundersnot available
KeywordsCLARITYScale (ratio)UsabilityDocumentationPerceptionTest (biology)Simulated patient

Abstract

fetched live from OpenAlex

Medical learners’ achievement emotions during educational activities remain unexamined in medical education research in Japan for many reasons, including a lack of assessment tools in Japanese that have been validated in Japanese medical educational contexts. The Medical Emotion Scale (MES) was developed to assess the achievement emotions of Canadian medical learners during learning activities in computer-based and live simulation environments. The goal of this study was to create and validate a Japanese version of the Medical Emotion Scale (J-MES). The MES was translated into Japanese using the Translation, Review, Adjudication, Pretest, and Documentation team translation model. We then conducted two initial validation studies of the J-MES. In the first pilot study, we asked five, native-Japanese, second-year medical student volunteers to assess their emotions with the J-MES during the same computer-based clinical reasoning activity that was used to develop the Canadian MES. Each participant was then interviewed to assess the clarity and suitability of the descriptions and the usability of the format. In a second larger study of the J-MES 41 Japanese medical students were recruited to assess their achievement emotions, appraisals, performances, and self-efficacy perceptions during the same the technology-based clinical reasoning task. We also conducted individual semi-structured interviews with ten of these participants to explore potential cross-cultural differences in achievement emotions between Japanese and North American medical students. Part 1 of the pilot study demonstrated that the descriptions contained in the J-MES were clear and that the scale captured an appropriate range of emotions perceived by students during the task. The second larger study revealed that emotions measured using the J-MES correlated with theoretically relevant constructs (e.g., appraisals, performance) of control-value theory. The results also revealed that the profiles and the internal structure of the scale were largely consistent with emotion theory. A few items, such as culture and learning environment (i.e., pride, compassion, and surprise), on the J-MES were found to be context-dependent. Our findings indicated that the emotions assessed using J-MES aligned with the control value theory and clearly demonstrated the scoring capacity, generalizability, and extrapolability of the J-MES. Further investigation is needed to confirm the robustness and cross-cultural validity of the J-MES to expand its applications

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)0.006
metaresearch head score (Gemma)0.010
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 score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.342
Teacher spread0.324 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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