A Community-Based Intervention to Increase Screening Mammography Among Disadvantaged Women at an Inner-City Drop-In Center
Bibliographic record
Abstract
PURPOSE: To determine the effectiveness of a community- based intervention to increase the use of screening mammography among disadvantaged women at an inner-city drop-in center. METHODS: This study involved women 50 to 70 years old who were clients of an inner-city drop-in center in Toronto, Canada, during the years 1995-2002 (N = 158 in 1995-2001 and N = 89 in 2002). In 2002, the drop-in center and a nearby hospital initiated a collaborative breast cancer screening project in which a staff member of the drop-in center accompanied small groups of women for mammography visits at a weekly pre-arranged time. Interrupted time series analysis was used to examine the effect of this intervention on the annual rate of screening mammography, as determined by review of medical records. RESULTS: More than half of the women 50 to 70 years old who used the drop-in center in 2002 had been diagnosed with a major mental illness, and one-third were either homeless or living in supportive housing. In the 7 years before the introduction of the intervention, annual mammography rates among women using the drop-in center averaged 4.7%. During the intervention year, 26 (29.2%) of 89 women underwent mammography (p = 0.0001 for the change from pre-to post-intervention). CONCLUSIONS: The introduction of accompanied small-group visits was associated with significantly increased use of mammography in a group of disadvantaged women who were clients of an inner-city drop-in center. This approach may be useful to promote breast cancer screening among women affected by mental illness or homelessness who have contact with community-based agencies.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".