Investigating the Relationship Between Health Literacy and Mental Health Status and the Utilization of Mental Health Services in Older Adults Diagnosed with Cancer: A Research Protocol
Bibliographic record
Abstract
Introduction: The utilization of mental health services has been shown to enhance treatment outcomes in older adults diagnosed with cancer, yet these services are underutilized. Health literacy level and mental health status of patients affect the degree to which they seek and utilize mental health services. Sociodemographic factors play a role in service use. The objective of the study is to examine how health literacy and mental health status impact the utilization of mental health services available among older adults diagnosed with cancer. Methods: A sample of 120 older adults > 65 diagnosed with cancer will be recruited. Health literacy will be measured using the Health Literacy Questionnaire (HLQ). Mental health status will be measured using the 12-Item General Health Questionnaire (GHQ-12). The utilization of mental health services will be measured using a 7-item self-administered questionnaire. The statistical association between mental health service utilization, mental health status and health literacy will be investigated using Analysis of Covariance (ANCOVA). A qualitative analysis will be conducted based on the COM-B model with open-ended questions and analyzed using thematic analysis. Results: A positive significant association between health literacy and mental heath service utilization is expected. An inverse significant association between mental health status and mental health service utilization is expected. It is anticipated that higher utilization of mental health services varies based on sociodemographic factors such as being female and having higher income. Discussion: The study reports that being female and of higher income are associated with greater use of mental health services, likely due to increased health behaviour changes and better access to resources among educated, higher income individuals. Conclusion: This study emphasizes the significant impact of health literacy and mental health status on the use of mental health services among older adults diagnosed with cancer. The importance of mental health services in the care of older adults highlights the need for further investigation in future research.
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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 | 0.039 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.032 | 0.007 |
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".