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Record W2069981921 · doi:10.1002/wps.20010

Religion and psychiatry: from conflict to consensus

2013· article· en· W2069981921 on OpenAlexaff
Marilyn Baetz

Bibliographic record

VenueWorld Psychiatry · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSpiritualityMental healthPerspective (graphical)PsychiatryMedicineResource (disambiguation)PsychotherapistPsychologyAlternative medicine

Abstract

fetched live from OpenAlex

Pargament and Lomax present religion as a “double-edged sword”, serving both as a resource and a challenge to psychiatry. This analogy may be useful in guiding further research and moving to a position of consensus a field that has been sometimes conflict-laden. Not only psychiatry has had difficulty with religion, but also religion — with its focus on mind, body and spirit — has had difficulty with psychiatry. Optimal restoration of mental health in a patient requires an ability by the psychiatrist to assemble evidence for treatment at levels ranging from cells to communities. This evidence is best informed by credible research. Religion is unique in that it provides a link to the past and to the future. In a society where governments focus on short-term expediency, many things are thrown away, families are volatile, institutions are unstable, and cultures are less “pure” because of migration, religion clearly provides a longitudinal perspective. To understand psychiatric disorders, a longitudinal perspective, including consideration of religion and spirituality, is also needed. In those individuals where religion and mental health problems intertwine, the better the understanding psychiatrists have of potentials and pitfalls around their patients’ religious or spiritual beliefs (or loss of them), the more able they will be to help restore balanced mental health. The author would like to thank Drs. R.C. Bowen and L. Balbuena for editorial assistance.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.326
Teacher spread0.304 · 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.

Study designNot applicable
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

Citations6
Published2013
Admission routes1
Has abstractyes

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