Self-Care Dimensions of Complementary and Alternative Medicine Use among Older Adults
Bibliographic record
Abstract
BACKGROUND: There is a lack of understanding about the patterns and rates of CAM use among older adults owing to a lack of research on specific types of CAM. OBJECTIVES: This study examines several dimensions of self-care deemed to be associated with CAM. Unmet health care needs, self-care attitudes, and spirituality are interpreted as health belief structures underlying CAM. METHODS: Logistic regression analysis was used to examine use of three groups of practitioner-based CAM: (a) chiropractic; (b) massage, and (c) acupuncture, homeopathy and/or naturopathy use. We analyze a subsample of 4,401 older adults drawn from the 1996/1997 and 1998/1999 waves of the Canadian National Population Health Survey. RESULTS: The logistic regression analyses indicate that self-care attitude and spirituality represent important predictors of practitioner-based CAM use. The associations for unmet health care needs were not supported. The strongest factors associated with CAM use were the illness context variables, which suggest that measures of need are key factors in leading individuals to seek other forms of health care. DISCUSSION: Practitioner-based CAM use among older adults is influenced by self-care attitude and spirituality, in addition to health status, but to varying degrees depending on the type of CAM. Support of these self-care facets suggests that there is a desire on the part of consumers to exercise choice and to participate in health care decisions when considering CAM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".