Alternative health care consultations in Ontario, Canada: A geographic and socio-demographic analysis
Bibliographic record
Abstract
BACKGROUND: An important but understudied component of Canada's health system is alternative care. The objective of this paper is to examine the geographic and socio-demographic characteristics of alternative care consultation in Ontario, Canada's largest province. METHODS: Data is drawn from the Canadian Community Health Survey (CCHS Cycle 3.1, 2005) for people aged 18 or over (n = 32,598) who had a consultation with an alternative health care provider. Four groups of consultations are examined: (1) all consultations (2) massage therapy (3) acupuncture, and (4) homeopath/naturopath. Descriptive statistics, mapping and logistic regression modeling are employed to analyze the data and to compare modalities of alternative health care use. RESULTS: In 2005, more than 1.2 million adults aged 18 or over consulted an alternative health care provider, representing about 13% of the total population of Ontario. The analysis revealed a varied geographic pattern of consultations across the province. Consultations were fairly even across the urban to rural continuum and rural residents were just as likely to consult a provider as their urban counterparts. From a health perspective, people with a chronic condition, lower health status and self-perceived unmet health care needs were more likely to see an alternative health provider. Women with chronic conditions such as fibromyalgia, high blood pressure, chronic fatigue syndrome and chemical sensitivities were more likely to see an alternative provider if they felt their health care needs were not being met. CONCLUSIONS: The analysis revealed that geography is not a factor in determining alternative health care consultations in Ontario. By contrast, there is a strong association between these consultations and socio-demographic characteristics particularly age, sex, education, health and self-perceived unmet health care needs. The results underscore the importance of women's health needs as related to alternative care use. The paper concludes that there is a need for more place-specific research that explores the reasons why people use specific types of alternative health care as tied to socio-economic status, health, place of residence, and knowledge of these treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".