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Record W1675706678 · doi:10.1089/acm.2013.0329

Complementary and Alternative Medicine Use in Infertility: Cultural and Religious Influences in a Multicultural Canadian Setting

2014· article· en· W1675706678 on OpenAlexafffundabout
Suzanne C. Read, Marie‐Eve Carrier, Rob Whitley, Ian Gold, Togas Tulandi, Phyllis Zelkowitz

Bibliographic record

VenueThe Journal of Alternative and Complementary Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicReproductive Health and Technologies
Canadian institutionsMcGill University Health CentreJewish General HospitalDouglas Mental Health University InstituteMcGill University
FundersCanadian Institutes of Health Research
KeywordsMedicineAlternative medicineHomeopathyMulticulturalismInfertilityThematic analysisFamily medicineFertilityTraditional medicineChiropracticHealth careQualitative researchPsychologySocial sciencePregnancy

Abstract

fetched live from OpenAlex

OBJECTIVES: To explore the use of complementary and alternative medicine (CAM) for infertility in a multicultural healthcare setting and to compare Western and non-Western infertility patients' reasons for using CAM and the meanings they attribute to CAM use. DESIGN: Qualitative semi-structured interviews using thematic analysis. SETTINGS/LOCATION: Two infertility clinics in Montreal, Quebec, Canada. PARTICIPANTS: An ethnoculturally varied sample of 32 heterosexual infertile couples. RESULTS: CAM used included lifestyle changes (e.g., changing diet, exercise), alternative medicine (e.g., acupuncture, herbal medicines), and religious methods (e.g., prayers, religious talismans). Patients expressed three attitudes toward CAM: desperate hope, casual optimism, and amused skepticism. PARTICIPANTS' CAM use was consistent with cultural traditions of health and fertility: Westerners relied primarily on biomedicine and used CAM mainly for relaxation, whereas non-Westerners' CAM use was often influenced by culture-specific knowledge of health, illness and fertility. CONCLUSIONS: Understanding patients' CAM use may help clinicians provide culturally sensitive, patient-centered care.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.151
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.086
GPT teacher head0.370
Teacher spread0.285 · 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 designObservational
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

Citations30
Published2014
Admission routes3
Has abstractyes

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