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Record W4243350806 · doi:10.32920/ryerson.14647806

Dementia narratives of emergency department nurses

2021· preprint· en· W4243350806 on OpenAlexaff
Negin P. Shalchi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicEducation, Healthcare and Sociology Research
Canadian institutionsToronto Metropolitan University
FundersAlzheimer's Society
KeywordsEmergency departmentDementiaNarrativeHealth careNursingEmergency nursingMedicineWork (physics)Medical emergencyPsychologyPolitical science

Abstract

fetched live from OpenAlex

The Emergency Department is often the main portal of entry for acutely ill elderly patients with dementia requiring healthcare. Emergency nurses assume responsibility for managing both critical illnesses and Behavioural and Psychological Symptoms of Dementia. Using narrative methodology and photographic images provided by participants, two emergency department nurses were interviewed to understand their experiences caring for patients with dementia. A Critical Social Theory lens facilitated an examination of how dominant discourse about work environment, power relations, and the healthcare system shape their stories of dementia. Findings indicated that nurses’ experiences are shaped by frustrations, threaten person-centred care, and are associated with limited time and knowledge and competing work demands when caring for patients with dementia. Study recommendations include the need for policy that supports culture change in the emergency department, investment in professional development of emergency department nurses, and infrastructure supports such as Geriatric Emergency Management nurses and nurse’s aides

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)0.005
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.007
Scholarly communication0.0050.006
Open science0.0010.007
Research integrity0.0020.004
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 head0.143
GPT teacher head0.519
Teacher spread0.376 · 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.

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
Published2021
Admission routes1
Has abstractyes

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