Predictors of mammography use among Canadian women aged 50-69: findings from the 1996/97 National Population Health Survey.
Bibliographic record
Abstract
BACKGROUND: Screening mammography, although recommended every 2 years for women aged 50-69, is thought to be underused among select groups of Canadian women. METHODS: We used data from the 1996/97 National Population Health Survey to describe current patterns in mammography use (including reasons for not having a mammogram within the 2 years before the survey and future screening intentions) in Canada and to determine factors associated with nonparticipation and time-inappropriate use (mammogram 2 or more years before the survey) among women aged 50-69. RESULTS: Among respondents aged 50-69, 79.1% (95% confidence interval [CI] 76.9%-81.2%) reported ever having had a mammogram, and 53.6% (95% CI 51.4%-55.9%) had had a recent (time-appropriate) mammogram (within the 2 years before the survey). Only 0.6% (95% CI 0.3%-0.9%) of recently screened women reported problems of access, and few reported personal or health system barriers as reasons for not obtaining a recent mammogram. Over 50% of the women who had not had a recent mammogram reported that they did not think it was necessary, and only 28.2% (95% CI 23.8%-32.7%) of those who had never had a mammogram planned to have one within the 2 years following the survey. The rate of time-appropriate mammography varied significantly by province, from 41.1% (95% CI 29.3%-52.9%) in Newfoundland to 69.4% (95% CI 61.3%-77.6%) in British Columbia. Significant predictors of never having had a mammogram included higher age, residence in a rural area, Asia as place of birth, no involvement in volunteer groups, no regular physician or recent medical consultations (including recent blood pressure check), current smoking, infrequent physical activity and no hormone replacement therapy. INTERPRETATION: Despite increases in mammography screening rates since the 1994/95 National Population Health Survey, current estimates indicate that almost 50% of women aged 50-69 have not had a time-appropriate mammogram. Our findings confirm continued low mammography participation rates among older women and those in rural areas, select ethnic groups and women with negative health care and lifestyle characteristics.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".