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Record W2528588697 · doi:10.1177/2333393616671076

Assessment of Capacity to Consent by Nurses Who Deliver Health Care to Patients Who Misuse Substances

2016· article· en· W2528588697 on OpenAlexaff
Darlene Taylor, Anita Ho, Louise C. Mâsse, Natasha Van Borek, Neville Li, Michelle Patterson, Gina Ogilvie, Jane A. Buxton

Bibliographic record

VenueGlobal Qualitative Nursing Research · 2016
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsSimon Fraser UniversityMcMaster UniversityUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
Fundersnot available
KeywordsOutreachNursingInterpersonal communicationContext (archaeology)Health carePsychologyQualitative propertyQualitative researchSample (material)Nonprobability samplingMedicineSocial psychologyEnvironmental healthSociology

Abstract

fetched live from OpenAlex

This qualitative study explored the current practice that nurses use to assess capacity to consent to health care (CTC-HC) in street outreach settings. Key informant interviews were conducted with a purposive sample of nurses from each of British Columbia's five regional health authorities, allowing nurses to describe their lived experiences with assessing CTC-HC. Content analysis was used to summarize information captured in the data. A total of 19 nurses participated in the study. Five themes emerged from the data: (a) internal guiding forces that contribute to the nurses' assessment, (b) external influences that contribute to the nurses' assessment, (c) measures that are important for assessing CTC-HC, (d) threshold setting, and (e) context (physical and interpersonal) within which assessment of capacity takes place. These elements will be incorporated into a capacity assessment tool that can be used in nursing best practices.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.590
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.230
GPT teacher head0.620
Teacher spread0.390 · 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.

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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