Impact of PRECEDE–PROCEED Model Audits in Cancer Screening Programs in Lombardy Region: Supporting Equity and Quality Improvement
Bibliographic record
Abstract
BACKGROUND: Health disparities related to socio-economic factors impact access to preventive health interventions. The PRECEDE-PROCEED model, a multidimensional approach to health promotion, has been adapted to optimise cancer screening programs in Lombardy, Italy, addressing these disparities. METHODS: This study evaluated the application of systemic audits based on the PRECEDE-PROCEED model across Lombardy cancer screening programs. A systematic region-wide audit was performed in 2019, and follow-up audits were performed in 2022-2023. Data were collected using structured analysis methodologies, including epidemiological, behavioural, and organisational assessments. RESULTS: The 2019 audit showed strengths in participation and quality standards but identified challenges in cervical cancer screening coverage and waiting times for assessments. Improvements plans included the digitisation of processes and stakeholder engagement. The 2022-2023 audits reported increased coverage for breast and colorectal screenings, but a slight decline in participation rates and examination coverage. Organisational improvements were noted, yet gaps in training and equity-targeted actions remained. CONCLUSION: The PRECEDE-PROCEED model audits led to notable improvements in the quality and equity of cancer screening programs in Lombardy. Sustained focus on digital integration, continuous re-training, and targeted equity interventions is essential for further progress.
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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 | 0.108 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".