The Instruments used to Assess Health Literacy of Prostate Cancer in Indigenous and Non-Indigenous Population: A Scoping Review
Bibliographic record
Abstract
Introduction: Prostate cancer is a major global public health concern, particularly in Canada where it is the third leading cause of cancer-related deaths among men. The economic burden associated with treating prostate cancer is substantial, and patients often experience complex and ongoing care, resulting in decreased quality of life. Indigenous populations face even greater challenges in accessing healthcare services and resources, leading to delayed diagnosis and treatment. Health literacy, the ability to understand and utilize health information, is a major predictor in quality of care and patient outcome. Despite the significance of health literacy and prostate cancer, the tools used to assess it have not been thoroughly assessed. Moreover, health literacy in indigenous populations with prostate cancer is particularly an understudied field. Aim: This scoping review aims to explore the existing health literacy tools used in prostate cancer cohorts, assess their quality, and identify gaps in the assessment of health literacy in Indigenous populations. Methods: A systematic search of Medline, Cumulative Index to Nursing and Allied Health Literature were performed and articles assessing health literacy in prostate cancer patients using a questionnaire were extracted. Results: Total of 421 articles were screened, resulting in the inclusion of 16 studies. The most employed questionnaire was the Rapid Estimate of Adult Literacy in Medicine (REALM) and its variants R-REALM and SF-REALM. Other tools included the Health Literacy Questionnaire (HLQ), Short-Test of Functional Health Literacy in Adults (S-TOFHLA), the Swedish Functional Health Literacy Scale (SFHL), Health Literacy for Iranian Adults (HELIA), Brief Health Literacy Screening tool (BHLS), and others. However, none of these tools were specifically designed for assessing health literacy in prostate cancer, and none have been validated in Indigenous populations. The domains that each questionnaire assessed were explored and their limitations were identified. Conclusion: This review provides a list of the measuring tools for prostate cancer-related health literacy. None of the tools used in the prostate cancer population were validated for indigenous people and did not consider the unique requirements of that population. Developing a tool for assessing health literacy of indigenous patients with prostate cancer has the potential to improve patient outcomes and decision-making, leading to better quality of care and disease prognosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".