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Record W2082418525 · doi:10.1093/sw/55.4.337

Cultural Considerations for Social Service Agencies Working with Muslim Clients

2010· article· en· W2082418525 on OpenAlexaffabout
John R. Graham, Catherine P. Bradshaw, Jennifer L. Trew

Bibliographic record

VenueSocial Work · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Spirituality, and Psychology
Canadian institutionsUniversity of British ColumbiaUniversity of Calgary
Fundersnot available
KeywordsSpiritualityPublic relationsSocial workFaithSociologyService providerCultural diversityDiversity (politics)Service (business)PerceptionCultural competenceQualitative researchSocial psychologyPsychologyPolitical scienceBusinessMedicineMarketingPedagogySocial science

Abstract

fetched live from OpenAlex

Social service agencies and practitioners are continually challenged to provide services that are effective for, and reflective of, cultural and religious diversity. Qualitative interviews were conducted with 50 social work practitioners in four Canadian cities to gain insight into methods of practice that are culturally sensitive for work with Muslim clients. No hard-and-fast rules are identified, but the findings point to some cultural values and issues to consider (in particular the roles of faith and spirituality, community, family, gender, and perceptions of service seeking) and ways that agencies can be responsive to the cultural needs of Muslim clients (in particular by understanding pathways to care, explaining cultural difference, and providing culturally sensitive services). Implications for agencies providing services and future research directions are discussed.

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)0.020
metaresearch head score (Gemma)0.031
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.031
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0310.016
Scholarly communication0.0110.003
Open science0.0030.010
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.120
GPT teacher head0.386
Teacher spread0.265 · 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

Citations55
Published2010
Admission routes2
Has abstractyes

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