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Record W3007568183 · doi:10.26522/ssj.v13i2.1836

Last Bastion Nevermore! A Qualitative Exploration of the Australian Government’s Fifth National Mental Health and Suicide Prevention Plan from the Perspective of Lessening Mental Stigma and Sanism in the Workplace

2020· article· en· W3007568183 on OpenAlexvenueno aff
Damian Mellifont

Bibliographic record

VenueStudies in Social Justice · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthStigma (botany)WorkforceInclusion (mineral)Qualitative researchGovernment (linguistics)Focus groupPublic relationsPsychologyNursingPolitical scienceMedicineSociologyPsychiatrySocial psychologySocial science

Abstract

fetched live from OpenAlex

The need to advance mental health through greater levels of social and economic inclusion represents a pressing policy issue. Within Australia, this policy focus has been progressed at a national level. This exploratory study aims to critically investigate The Fifth National Mental Health and Suicide Prevention Plan in terms of its potential to help reduce mental stigma and discrimination within Australian workplaces. Qualitative content analysis was applied to the national policy document as well as to 12 academic texts retrieved from a Google Scholar search and meeting inclusion criteria. Stage one of the content analysis process revealed themes of representation, education, research, and activism, while stage two added those of language, legal, and media. This study posits that workplace anti-mental stigma and sanism measures as identified within the Plan are limited in the sense that they represent only a subset of those currently available. This research also supports the prospect of these measures operating in a collaborative manner. Finally, it is proposed that potential exists throughout Australian workplaces to implement stigma and sanism reduction measures that specifically target the health and peer workforce.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.109
Threshold uncertainty score0.849

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.273
GPT teacher head0.510
Teacher spread0.238 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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