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Record W68372233

Social Work Practice in Mental Health: cross-cultural perspectives

2015· book· en· W68372233 on OpenAlexfundno aff
Abraham Francis, Paula La Rosa, Lakshmi Sankaran, S. Rajeev

Bibliographic record

VenueResearchOnline at James Cook University (James Cook University) · 2015
Typebook
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaMahatma Gandhi UniversityQueensland University of TechnologyJames Cook UniversitySingapore Police Force
KeywordsMental healthPower (physics)ReflexivitySociologyTeamworkWork (physics)MindfulnessPsychologyPublic relationsEngineering ethicsSocial sciencePolitical sciencePsychotherapistEngineering
DOInot available

Abstract

fetched live from OpenAlex

This book represents the sharing of knowledge and experiences that is cross-cultural, cross-disciplinary and across countries. It aims bringing to the social work practitioner a wealth of understanding about situations, practices and cultures that could not possibly have been experienced first-hand about mental health. The book provides cross cultural perspectives on recovery; strengths based practice, mindfulness, disaster & mental health, community mental health and other related aspects. These contributions from across the world, from different cultures, and from vastly different experiences are a celebration of the global practice of social work. The series of chapters in this book makes a contribution to a deeper understanding of various facets of social work in mental health. The complexities elucidated here can be addressed by embracing the power of teamwork, the power of visionary leadership and the power of reflexivity. The book offers an opportunity for practitioners to explore all these in detail.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.056
GPT teacher head0.424
Teacher spread0.368 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations12
Published2015
Admission routes1
Has abstractyes

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