Who are the under- and never- screened for cancer in Ontario: a qualitative investigation
Bibliographic record
Abstract
BACKGROUND: Observed breast, cervical and colon cancer screening rates are below provincial targets for the province of Ontario, Canada. The populations who are under- or never-screened for these cancers have not been described at the Ontario provincial level. Our objective was to use qualitative methods of inquiry to explore who are the never- or under-screened populations of Ontario. METHODS: Qualitative data were collected from two rounds of focus group discussions conducted in four communities selected using maps of screening rates by dissemination area. The communities selected were archetypical of the Ontario context: urban, suburban, small city and rural. The first phase of focus groups was with health service providers. The second phase of focus groups was with community members from the under- and never-screened population. Guided by a grounded theory methodology, data were collected and analyzed simultaneously to enable the core and related concepts about the under- and never-screened to emerge. RESULTS: The core concept that emerged from the data is that the under- and never-screened populations of Ontario are characterized by diversity. Group level characteristics of the under- and never-screened included: 1) the uninsured (e.g., Old Order Mennonites and illegal immigrants); 2) sexual abuse survivors; 3) people in crisis; 4) immigrants; 5) men; and 6) individuals accessing traditional, alternative and complementary medicine for health and wellness. Under- and never-screened could have one or multiple group characteristics. CONCLUSION: The under- and never-screened in Ontario comprise a diversity of groups. Heterogeneity within and intersectionality among under- and never-screened groups adds complexity to cancer screening participation and program planning.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".