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Record W4385791363 · doi:10.1136/ebm-2023-pod.93

93 Overdiagnosis and decisional stakes? A family medicine resident perspective on shared decision making

2023· article· en· W4385791363 on OpenAlexaboutno aff
Amrita Sandhu, Roland Grad

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsOverdiagnosisContext (archaeology)Session (web analytics)PsychologyQualitative researchPerspective (graphical)MedicineFamily medicineInternal medicineComputer science

Abstract

fetched live from OpenAlex

Objectives Describe what Family Medicine (FM) residents consider to be high versus low stakes situations when engaging patients in shared decision making (SDM), and whether overdiagnosis is a factor in their mind. Method A sequential explanatory mixed methods study was conducted with 54 first year FM residents at McGill University who attended an academic-half day session about SDM. Quantitative: Immediately before this session, residents were asked to complete a 7-item version of IncorpoRATE, a measure of clinician willingness to engage in SDM. Qualitative: Using extreme case-sampling based on IncorpoRATE responses, 16 residents were interviewed. We asked what situations they considered to be high versus low stakes for SDM in the context of decision making in primary care. Integration: Qualitative and quantitative findings were compared and combined to elucidate resident perspectives on decisional stakes. Interview transcripts were analyzed for any mention of the potential role that overdiagnosis plays in shared decision making, in relation to situations perceived as high stakes by residents. Results Overall, residents were willing to engage in SDM, with mean willingness scores of 7.32 [1.52]. However, resident willingness to engage in shared decision making varied widely, from 4.38 to 9.19 out of a maximum score of 10. Qualitative findings revealed variation in what types of decisions residents considered low and high stakes for SDM. For example: Mammography screening for breast cancer was considered a low stakes situation. No consensus existed as to whether PSA screening for prostate cancer was a high or low stakes situation. Symptomatic patients who declined diagnostic testing was described as a high stakes situation. Conclusions Our sample of family medicine residents did not refer to the concept of overdiagnosis when discussing their perceptions of the stakes for healthcare decision making. This suggests postgraduate medical education at our university must be re-examined in relation to improving learning on this topic. More work is needed to understand the stakes of situations for prioritizing SDM in Family Medicine.

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)0.015
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.009
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0020.003
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 head0.393
GPT teacher head0.525
Teacher spread0.132 · 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 designQualitative
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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Citations0
Published2023
Admission routes1
Has abstractyes

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