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Record W2141345046 · doi:10.1017/s1041610210001328

Age-related patterns in mental health-related complementary and alternative medicine utilization in Canada

2010· article· en· W2141345046 on OpenAlexaffabout
Rebecca Crabb, John Hunsley

Bibliographic record

VenueInternational Psychogeriatrics · 2010
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMental healthContext (archaeology)Logistic regressionMedicineGerontologyHealth careAge groupsPsychiatryDemography

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to examine whether age-related differences in rates of use of complementary and alternative medicine (CAM) specifically for mental health problems parallel well-known age-related differences in use of conventional mental health services and medications. METHODS: A sample of middle-aged (45-64 years; n = 10,762), younger-old (65-74; n = 4,113) and older-old adults (75 years and older; n = 3,623) was drawn from the 2001-2002 Canadian Community Health Survey (CCHS), Cycle 1.2, Mental Health and Wellbeing. Age-related utilization rates of conventional and complementary mental health services and medications/products were calculated. Logistic regression analyses were used to examine the strength of association between age group and utilization of services and medications or products in the context of other important sociodemographic and clinical characteristics. RESULTS: When considered in the context of other sociodemographic and clinical characteristics, older age was positively associated with mental health-related utilization of alternative health products. Older age was not significantly associated with mental health-related consultations with CAM providers. CONCLUSIONS: Overall, age-related patterns in mental health-related use of CAM did not directly correspond to age-related patterns in conventional mental health care utilization, suggesting different sets of predictors involved in seeking each type of 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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.240
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
Bibliometrics0.0000.000
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 head0.039
GPT teacher head0.353
Teacher spread0.314 · 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 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

Citations4
Published2010
Admission routes2
Has abstractyes

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