A qualitative study examining older adult usage of complementary and alternative medications and natural health products for cognitive and mental health improvement
Bibliographic record
Abstract
OBJECTIVES: The use of complementary and alternative medications (CAM) and natural health products (NHPs) to enhance mood and reduce cognitive decline is well known yet research guiding Older Adult patients and their health care teams on such usage is limited. The purpose of this study was to understand CAM and NHP usage patterns for cognitive health among healthy Older Adults to enable the construct of better guidelines for health care practitioners regarding such usage. METHODS: A qualitative study was designed to determine usage patterns of CAM and NHPs among Older Adults. Participants were recruited from the Center for Elder Research at Sheridan College and open-ended questions were utilized during one-on-one interviews with interested participants to determine usage patterns in relation to cognitive health. RESULTS: A total of 10 participants completed the interviews with an age range of 63-86 years. Results were analyzed using the triangulation method based on a Grounded Theory approach to identify 6 main emerging themes: fear of cognitive health decline, strategies to mitigate perceived/self-diagnosed cognitive health decline, dietary influences on cognitive health, healthcare ownership and self-care, CAM and NHPs loyalty, and supporting Older Adult CAM and NHPs use. Percentages of each response within each main theme was also determined. CONCLUSIONS: Older Adults appeared to be cognizant of their cognitive health and tended to rely on CAM and NHPs to improve self-perceived decline in cognitive health. Older Adults expressed a desire for their health care practitioners to become more accommodating of such usage and for the government to support them financially for expenditures on CAM and NHPs. Older Adults indicated a preference of visiting health care practitioners who supported the use of these products.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".