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Record W2913988486 · doi:10.11575/prism/35751

The State of Patient Education in the Emergency Department of a Western Canadian Urban Hospital: A Case Study

2019· dissertation· en· W2913988486 on OpenAlexaboutno aff
Mehran Niayesh

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typedissertation
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency departmentState (computer science)MedicineMedical emergencyState hospitalFamily medicineEmergency medicinePolitical scienceNursingComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

In this qualitative, instrumental case study, I sought to determine the state of patient education regarding present illness and care plan of patients, while they stayed in the emergency department for treatment and care in a Western Canadian urban hospital. I explored the state of patient education in an emergency department from the perspective of social constructivism. In this research, several sets of data were collected to increase the variety of data and participants. I gathered data from healthcare professionals (physicians and nurses) of the emergency department via questionnaires and also from patients by semi-structured interviews. Also I added my own observations as researcher to the data and used for analysis and discussion. Further, looking through a constructivism lens, I identified the factors that were experienced as challenges to the education processes and I suggested ways in which they might be ameliorated. Likewise, I determined the concerns of physicians, nurses, and patients regarding the process of patient education such as language barrier, time constraints, and lack of educational materials and equipment in the emergency department. In addition, this research sought suggestions from the healthcare professionals and patients to improve the process of patient education. After careful analysis of the findings of this research, I presented recommendations for healthcare professionals, for patients, for managers of the emergency department, and for further research in the area of patient education in the 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)0.000
metaresearch head score (Gemma)0.000
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 score0.335
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.238
Teacher spread0.232 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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