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Caller self‐care decisions following teletriage advice

2012· article· en· W1872189465 on OpenAlexaff
Bev Williams, Sharon Warren, Robert McKim, Wonita Janzen

Bibliographic record

VenueJournal of Clinical Nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity of AlbertaAlberta Health Services
Fundersnot available
KeywordsPopularityNursingDescriptive statisticsAdvice (programming)MedicineHealth careSample (material)Family medicinePsychologySocial psychology

Abstract

fetched live from OpenAlex

AIM: To examine caller self-care decisions following teletriage advice provided by nurses. BACKGROUND: The use of teletriage is gaining popularity as one way of enhancing capacity for self-care. Research from several countries suggests that teletriage reduces the use of other healthcare services without compromising safety. However, there is little or no research related to how often self-care advice is provided and whether or not callers follow the advice. DESIGN: A descriptive survey design was used with a random sample of 312 callers who were advised by a teletriage nurse to engage in self-care. METHOD: Callers were randomly selected from all calls to a teletriage service each day of the month for nine months. Data were collected using a researcher-developed interview guide and analysed using a variety of inferential statistics for forced choice questions and content analysis for open-ended questions. RESULTS: The majority of callers who were advised to engage in self-care reported doing so. Callers with greater self-efficacy and satisfaction with the nurse interaction were more likely to follow advice to self-care. All callers would call the teletriage service again for the same or a different issue. CONCLUSION: Teletriage callers were confident in the advice provided and were willing to continue to use the service. RELEVANCE TO CLINICAL PRACTICE: This study indicates that teletriage programmes are a cost-effective way of addressing self-care needs of individuals who might otherwise visit an emergency department.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.096
GPT teacher head0.515
Teacher spread0.419 · 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 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

Citations30
Published2012
Admission routes1
Has abstractyes

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