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Record W4412635964 · doi:10.18192/aporia.v17i2.7298

Mental health nursing and the negotiated order of professions: A critical analysis of public discourses related to psychotherapy in Quebec (Canada)

2025· article· en· W4412635964 on OpenAlexaffvenueabout
Pierre Pariseau‐Legault, Ricardo A. Ayala, Lisandre Labrecque‐Lebeau, Sandrine Vallée‐Ouimet, Audrey Bujold, Christine Gervais

Bibliographic record

VenueAporia · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité de MontréalUniversité du Québec en Outaouais
Fundersnot available
KeywordsOrder (exchange)Mental healthMental health nursingNursingPsychologySociologyPsychotherapistMedicineBusiness

Abstract

fetched live from OpenAlex

Access to mental health care remains a pressing global issue. In response, policymakers have devised strategies that span from self-care to psychotherapy, hoping to ease the strain. However, reforms over the past two decades have significantly restricted access to psychotherapy, limiting the number of professionals, such as nurses, allowed to practise under stringent conditions. This article examines the effects of Quebec’s public policies on the practice of psychotherapy and mental health interventions. A critical discourse analysis, grounded in Strauss’s theory of negotiated order, was conducted on 48 policy documents and public discourses. The findings reveal that mental health interventions have become disconnected from their therapeutic essence, reduced instead to technical tasks. This shift perpetuates a hierarchical professional landscape, subordinating these practices despite their reliance on the relational dynamics that define effective mental health care. For the nursing profession, the implications are profound. The profession’s contribution to providing timely access to community-based mental health services is being overlooked, stymied by outdated perceptions and policy restrictions.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.083
GPT teacher head0.510
Teacher spread0.428 · 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 designTheoretical or conceptual
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
Published2025
Admission routes3
Has abstractyes

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