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Record W2345470983 · doi:10.1111/jocn.13296

The emergency patient's participation in medical decision‐making

2016· article· en· W2345470983 on OpenAlexaff
Li‐Hsiang Wang, Suzanne Goopy, Chun‐Chih Lin, Alan Barnard, Chin‐Yen Han, Hsueh‐Erh Liu

Bibliographic record

VenueJournal of Clinical Nursing · 2016
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsUniversity of Calgary
FundersChang Gung Memorial Hospital, Linkou
KeywordsBricolageQualitative researchEmergency departmentGrounded theoryHealth careMedicineClinical decision makingMedical emergencyPsychologyNursingFamily medicineSociologyPolitical science

Abstract

fetched live from OpenAlex

AIMS AND OBJECTIVES: The purpose of this research was to explore the medical decision-making processes of patients in emergency departments. BACKGROUND: Studies indicate that patients should be given enough time to acquire relevant information and receive adequate support when they need to make medical decisions. It is difficult to satisfy these requirements in emergency situations. Limited research has addressed the topic of decision-making among emergency patients. DESIGN: This qualitative study used a broadly defined grounded theory approach to explore decision-making in an emergency department in Taiwan. METHODS: Thirty emergency patients were recruited between June and December 2011 for semi-structured interviews that were audio-taped and transcribed verbatim. RESULTS: The study identified three stages in medical decision-making by emergency patients: predecision (interpreting the problem); decision (a balancing act) and postdecision (reclaiming the self). Transference was identified as the core category and pattern of behaviour through which patients resolved their main concerns. This transference around decision-making represents a type of bricolage. CONCLUSIONS: The findings fill a gap in knowledge about the decision-making process among emergency patients. RELEVANCE TO CLINICAL PRACTICE: The results inform emergency professionals seeking to support patients faced with complex medical decision-making and suggest an emphasis on informed patient decision-making, advocacy, patient-centred care and in-service education of health staff.

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)0.005
metaresearch head score (Gemma)0.046
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.046
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.326
GPT teacher head0.616
Teacher spread0.289 · 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 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

Citations12
Published2016
Admission routes1
Has abstractyes

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