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Record W2416359163 · doi:10.3233/978-1-61499-574-6-20

Physician Experiences with Perceived Pressure to Order Diagnostic Imaging Services

2015· article· en· W2416359163 on OpenAlexaffabout
Janessa Griffith, Helen Monkman, Elizabeth M. Borycki, André Kushniruk

Bibliographic record

VenueStudies in health technology and informatics · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsFeelingOrder (exchange)Medical imagingEXPOSEMedicinePsychologyKnowledge managementBusinessRadiologyComputer scienceSocial psychologyFinance

Abstract

fetched live from OpenAlex

The overuse of diagnostic imaging (DI) services, which is estimated to be 30% in Canada, can expose patients to unnecessary radiation, and strain human and financial resources. This study explored the DI ordering practices of physicians in Canada through semi-structured interviews to gain a deeper understanding of the factors contributing to the overuse of DI services. The majority of participants (n=11; 91%) described feeling pressured by patients to order DI services in circumstances that were unwarranted. The results are followed by a discussion about ways technology (such as a decision support system) could aid in facilitating a dialogue between physicians and patients about when and when not to order DI.

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)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.201

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.004
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.355
GPT teacher head0.535
Teacher spread0.181 · 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.

Study designObservational
DomainMethods
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

Citations12
Published2015
Admission routes2
Has abstractyes

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