Promoting prostate cancer screening equity: Findings from a quality improvement education initiative implemented in 3 sites.
Bibliographic record
Abstract
112 Background: Prostate cancer (PC) screening guidelines differ in recommended age at screening and varying emphasis on risk factors (e.g., race, family history), leading to lack of clarity among healthcare providers (HCPs). Research has highlighted a higher risk for Black patients, and the importance for at-risk patients to undergo screening at an earlier age and receive appropriate follow-up. A system-based Quality Improvement Education (QIE) intervention was developed to increase screening and referrals for PC especially among higher risk subgroups. The QIE aimed to increase awareness of the burden and consequences of racial disparities while mobilizing a team-focused approach. Methods: QIE intervention was deployed at 3 practice sites that provide community-based primary care services to Black populations (72% Black patients). The sites differed in patient capacity, staff, challenges faced, and pre-intervention screening practices. Practice assessments were completed pre-intervention by site representatives (n = 3) and individual baseline surveys were filled out by HCPs (n = 24). The 12-week intervention included educational materials from Prostate Health Education Network (PHEN) and deployment of an updated screening protocol within each clinic to raise PC awareness to staff and patients. Post-intervention evaluation was based on qualitative interviews (n = 5) and feedback from the QIE coaches. Patient data from electronic health records (EHR) on PC screening and referrals was collected post-initiative and divided a posteriori into 3 sub-groups, pre, during and post-intervention (n = 6662). Results: QIE led to increased awareness of barriers to access faced by patients from diverse communities. The QIE also led to increased awareness among team members regarding the need for screening at-risk groups at an earlier age and the importance of follow-up with patients. Online education materials made available to HCPs raised patient awareness. Table highlights an increase in PC screening during the intervention, but limited sustainability post-intervention. Interviewees reported increases in patient education, referrals and follow-up action. Conclusions: Increase in percentage of patients screened during the intervention phase was potentially due to added attention during the initiative, while limited sustainability post-intervention might be due to the brief intervention period, reliance on retrospective data and inability to fully leverage EHR data. Based on this projects learnings, similar initiatives should seek organizational support for data analytics and process documentation to ensure consistent data standards and overall success.[Table: see text]
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 | 0.020 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".