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Record W2060794545 · doi:10.1155/2013/184024

An Analysis of Canadian Psychiatric Mental Health Nursing through the Junctures of History, Gender, Nursing Education, and Quality of Work Life in Ontario, Manitoba, Alberta, and Saskatchewan

2013· article· en· W2060794545 on OpenAlexaffabout
Mary Smith, Nazilla Khanlou

Bibliographic record

VenueISRN Nursing · 2013
Typearticle
Languageen
FieldNursing
TopicNursing education and management
Canadian institutionsYork UniversityQueen's University
Fundersnot available
KeywordsMental healthNursingMiddle Eastern Mental Health Issues & SyndromesCommissionHealth careNurse educationPsychologyMedicineMental health lawPsychiatryPolitical science

Abstract

fetched live from OpenAlex

A society that values mental health and helps people live enjoyable and meaningful lives is a clear aspiration echoed throughout our Canadian health care system. The Mental Health Commission of Canada has put forth a framework for a mental health strategy with goals that reflect the virtue of optimal mental health for all Canadians (Mental Health Commission Canada, 2009). Canadian nurses, the largest group of health care workers, have a vital role in achieving these goals. In Canada, two-thirds of those who experience mental health problems do not receive mental health services (Statistics Canada, 2003). Through a gendered, critical, and sociological perspective the goal of this paper is to further understand how the past has shaped the present state of psychiatric mental health nursing (PMHN). This integrative literature review offers a depiction of Canadian PMHN in light of the intersections of history, gender, education, and quality of nursing work life. Fourteen articles were selected, which provide a partial reflection of contemporary Canadian PMHN. Findings include the association between gender and professional status, inconsistencies in psychiatric nursing education, and the limitations for Canadian nurse practitioners to advance the role of the psychiatric mental health nurse practitioner.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.036
GPT teacher head0.317
Teacher spread0.281 · 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

Citations13
Published2013
Admission routes2
Has abstractyes

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