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Record W4310349204 · doi:10.3998/jmmh.2057

Healing Through Faith: The Role of Spiritual Healers in Providing Psychosocial Support to Canadian Muslims

2022· article· en· W4310349204 on OpenAlexaffabout
Mohamed Ibrahim, Fareed Mojab

Bibliographic record

VenueJournal of Muslim Mental Health · 2022
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsychosocialFaithRefugeeImmigrationContext (archaeology)Psychosocial supportPsychologyQualitative researchSocial supportMedicineNursingPolitical scienceSociologyPsychotherapistGeography

Abstract

fetched live from OpenAlex

Studies have documented on the role of religious leaders in providing psychosocial support to their members. However, there is a dearth of research in understanding the role imams play among Muslim communities in the Canadian context. The few studies that were undertaken in Europe and the United States revealed that imams played a significant role in addressing the psychosocial needs of their congregants, and this role increased in the post-9/11 era.This study explored experiences of imams in the provision of psychosocial support to Muslim Canadians, including new immigrants and refugees. We conducted in-depth, one-on-one interviews with faith leaders in a major metropolitan Canadian city. The data was transcribed and thematically analyzed using NVIVO. The study revealed that spiritual healing is considered the first line of care for psychosocial illness, and imams are considered the primary support network. The findings revealed that war-related traumas and post-resettlement challenges have significant impact on family functions and well-being.This study highlighted the need for culturally appropriate psychosocial support services for Muslim Canadians, including new immigrants and refugees. It also calls for better collaboration between service agencies and faith-based organizations in the communities to address 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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.163
Threshold uncertainty score0.814

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.030
GPT teacher head0.360
Teacher spread0.329 · 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 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

Citations5
Published2022
Admission routes2
Has abstractyes

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