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Record W4393091234 · doi:10.3389/fpsyt.2024.1349476

Empowering Chinese university health service providers to become mental health champions: insights from the ACE-LYNX intervention

2024· article· en· W4393091234 on OpenAlexafffund
Fenghua Wang, Suyu Hao, Ka Tat Tsang, Josephine Pui‐Hing Wong, Kenneth Fung, Alan Tai-Wai Li, Cun-Xian Jia, Shengli Cheng

Bibliographic record

VenueFrontiers in Psychiatry · 2024
Typearticle
Languageen
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsRegent Park Community Health CentreToronto Metropolitan UniversityUniversity of Toronto
FundersCanadian Institutes of Health ResearchNational Natural Science Foundation of China
KeywordsMental healthEmpowermentMental health literacyIntervention (counseling)Focus groupNursingPsychologyService providerMedicineMedical educationMental illnessService (business)PsychiatryPolitical scienceSociologyBusiness

Abstract

fetched live from OpenAlex

Background: Evidence shows that there is a high prevalence of mental health challenges including depression and anxiety, among Chinese university students. Providing mental health care providers with professional training is crucial to implementing effective mental health promotion initiatives in university settings. Globally, the focus of the mental health system is shifting to recovery and the importance of empowerment is increasingly being emphasized. There is a call to integrate empowerment education into professional training programs for health service providers with the goal of mobilizing them to become mental health champions capable of advancing mental health care. Method: The ACE-LYNX (Acceptance and Commitment to Empowerment-Linking Youth and Xin i.e., "heart(s)" in Chinese) intervention took place at six universities in Jinan, Shandong Province, China. It aimed to promote mental health literacy and build capacity among mental health service providers (MHSPs) to enable them to become mental health champions at their universities and beyond. A total of 139 university MHSPs participated. We collected pre-, immediate post- and three-month-post-surveys. In addition, we recruited forty-five participants to take part in three-month- post-intervention focus group interviews to explore their experiences taking part in ACE-LYNX and applying the knowledge, skills, and insights they gained from the intervention. Result: This paper reports on the effects of empowerment education, which is a key component of ACE-LYNX, on the MHSPs. Four themes were identified: 1) conscious awareness and behavioral change through psychological empowerment users; 2) professional insights and motivation for organizational empowerment; 3) non-self in the continuum of collective empowerment; and 4) interdisciplinary challenges and divergences in empowerment action. Discussion: We found that it is critical to integrate empowerment education into professional training. The process of MHSPs developing their empowerment practice is characterized by their moving from individual to collective empowerment along a continuum, with organizational and collective empowerment taking place in a longer time frame. Experiential learning, empathy education, and critical reflection accelerated the continuous iterative transformative process of empowerment practices. To advance the integration of empowerment into mental health care, the engagement of organizational decision-makers and policy makers in empowerment training is critical to ensure alignment of empowerment values and competence at all levels of service provision.

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)0.004
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.347
Teacher spread0.335 · 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 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

Citations4
Published2024
Admission routes2
Has abstractyes

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