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Record W4320477894 · doi:10.3998/jmmh.142

Title Pending 142

2023· article· en· W4320477894 on OpenAlexaffabout
Mohamed Ibrahim

Bibliographic record

VenueJournal of Muslim Mental Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsContext (archaeology)Style (visual arts)PsychosocialMillerPsychologyHistoryArtLiteratureGeologyArchaeologyPsychotherapist

Abstract

fetched live from OpenAlex

This is an accepted article with a DOI pre-assigned that is not yet published.Introduction: Studieshave documented on the role of religious leaders in providing psychosocial supportto their members. However, there is a dearth of research in understanding therole imams play among Muslim communities in the Canadian context. The fewstudies undertaken in Europe and the United States revealed that imams play asignificant role in addressing the psychosocial needs of their congregants andthis role increased in post 9/11 era.Objectives: This study explored experiences of imams in theprovision of psychosocial support to Muslim Canadians including new immigrantsand refugees. Methods: In-depth 1:1 interviews with faith leaders ina major metropolitan Canadian city was done. The data was transcribed andthematically analyzed using NVIVO. Results: The study revealed that spiritual healing isconsidered as first line of care for psychosocial illness, and, imams areconsidered as the primary support network. The findings revealed that war-relatedtraumas and post-resettlement challenges have significant impact on familyfunctions and wellbeing. Conclusion: Thisstudy highlighted the need for culturally appropriate psychosocial supportservices for Muslim Canadians including new immigrants and refugees.  It also calls for better collaborationbetween service agencies and faith-based organizations in the communities toaddress these specific needs. 

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 categoriesnone
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 score0.532
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.092
GPT teacher head0.464
Teacher spread0.373 · 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 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

Citations0
Published2023
Admission routes2
Has abstractyes

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