Functional health literacy and cancer care conversations in online forums for retired persons
Bibliographic record
Abstract
Cancer is primarily a chronic disease of older adults that must be managed and incorporated into everyday activities. Online sites are important sources of health care information and support. Health literacy is necessary for full utilisation of online resources. The objective of this study was to examine and compare cancer related conversations in online forums hosted by Canadian and American associations for retired persons. A content analysis was used to evaluate archived cancer conversations of general-health online forums representing two leading North American associations for retired persons. There were 125/1817 (6.8%) Canadians and 70/892 (7.8%) US cancer discussions among participants in 2006. Online conversations were grouped into three categories: request for information, provision of information and sharing of information. Important subthemes included cancer prevention and screening, treatment and cancer care and health system issues. There were significantly more posts about provision of cancer information from the Canadian compared with the US site (p = 0.023). American more than Canadian conversations emphasised the health system concerns as determinants of cancer care practices. Online discussion forums hosted by retirement associations may serve as an important channel in information dissemination about cancer prevention and screening, treatment and care support and health care advice for seniors.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".