Using Alternative Therapies to Manage Chronic Illness Among Older Adults: An Examination of the Health Context, Predisposing and Enabling Processes
Bibliographic record
Abstract
ABSTRACT This paper examines use of alternative therapies to manage a chronic illness among older adults with at least one of three major conditions: arthritis, heart disease, and hypertension. Drawing from developments in the health utilization literature, a focus is placed on the illness context, predisposing factors, and several factors deemed to enable persons to use complementary medicine. The baseline data (n = 879) from the 1995–96 North Shore Self-Care Study conducted in Vancouver, Canada were used for this study. Two dependent variables were analysed using logistic regression techniques – the first is based on a comprehensive question about using alternative therapies (such as herbal remedies, acupuncture, massage therapy, etc.) to manage a chronic condition; and the second uses a more specific question pertaining to meditation or praying. The results from the first analysis show that being younger, suffering from arthritis compared to hypertension, comorbidity, taking fewer medications, lower income, reading on the chronic condition, and the interaction between reading and illness self-efficacy are associated with trying alternative therapies. The findings for the second analysis show that being female, being younger, and not married, as well as reporting a more serious condition, illness duration and the interaction between having moderate levels of mutual aid and number of confidants result in a greater likelihood of trying meditation/prayer. Implications of these results are discussed in terms of their theoretical import, and their relevance for the degree to which unconventional and conventional medicine are complementary.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".