Targeting services to reduce social inequalities in utilisation: an analysis of breast cancer screening in New South Wales
Bibliographic record
Abstract
BACKGROUND: Many jurisdictions have used public funding of health care to reduce or remove price at the point of delivery of services. Whilst this reduces an important barrier to accessing care, it does nothing to discriminate between groups considered to have greater or fewer needs. In this paper, we consider whether active targeted recruitment, in addition to offering a 'free' service, is associated with a reduction in social inequalities in self-reported utilization of the breast screening services in NSW, Australia. METHODS: Using the 1997 and 1998 NSW Health Surveys we estimated probit models on the probability of having had a screening mammogram in the last two years for all women aged 40-79. The models examined the relative importance of socio-economic and geographic factors in predicting screening behaviour in three different needs groups - where needs were defined on the basis of a woman's age. RESULTS: We find that women in higher socio-economic groups are more likely to have been screened than those in lower groups for all age groups. However, the socio-economic effect is significantly less among women who were in the actively targeted age group. CONCLUSION: This indicates that recruitment and follow-up was associated with a modest reduction in social inequalities in utilisation although significant income differences remain.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".