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Fruitful collaborations with religious and spiritual communities to foster mental health in general society: An international perspective

2021· book-chapter· en· W4212928423 on OpenAlexaboutno aff
Wai Lun Alan Fung, Victor Shepherd, King Yee Agatha Chong, Sujatha D. Sharma, Avdesh Sharma

Bibliographic record

VenueOxford University Press eBooks · 2021
Typebook-chapter
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsFaithMental healthGeneral partnershipPerspective (graphical)Mental health careSpiritual careSpiritualityPublic relationsPsychologySociologyPolitical scienceMedicinePsychiatryAlternative medicineLaw

Abstract

fetched live from OpenAlex

Collaborations between mental health professionals (including psychiatrists) and spiritual care professionals/members of faith communities have been recommended by the World Psychiatric Association (WPA) and several national psychiatric organizations – to help attain high quality and equitable mental health care. Nonetheless, some are concerned about potential harms of such collaborations. It is imperative that such collaborations be ethical and person-centred. The diverse range of individuals who might be regarded as members of spiritual/religious communities also leads to significant variations in how these collaborations may occur. While the American Psychiatric Association (APA) Mental Health and Faith Community Partnership has presented one such model of collaboration at the national level, this chapter endeavours to illustrate the diverse forms, levels, cultural and geographical contexts of how these collaborations may occur – through the perspectives of mental health and spiritual care professionals from Canada, Hong Kong and India. Implications from these examples are also explored.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.013
Scholarly communication0.0080.007
Open science0.0010.009
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.048
GPT teacher head0.316
Teacher spread0.268 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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