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Record W2964097608 · doi:10.1017/s0714980819000382

How Do Regulated Nurse Professionals in Alberta Assess Geriatric Depression in Residential Care Facilities?

2019· article· en· W2964097608 on OpenAlexaffabout
Anna Azulai, Christine A. Walsh

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2019
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsUniversity of CalgaryMacEwan University
Fundersnot available
KeywordsNursingDepression (economics)Mental healthExploratory researchResidential careHealth professionalsMedicineGerontological nursingMental health careHealth carePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Although geriatric depression is a prevalent, serious, and under-recognized mental health condition in residential care facilities, there is a dearth of related research in Canada. This exploratory mixed methods study examines the perspectives and practices of regulated nurse professionals on assessment of geriatric depression in residential care facilities in Alberta. Findings from the quantitative surveys (n = 635) and qualitative interviews (n = 14) suggest that geriatric depression is not systematically assessed in these care settings due to multiple challenges, including confusing assessment protocol, inconsistent use and contested clinical utility of current assessment methods in facilities, limited availability of mental health professionals in facilities, and the varied views of regulated nurse professionals on who is responsible for depression assessment in facilities. Implications and future research directions are discussed.

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)0.007
metaresearch head score (Gemma)0.024
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.547

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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