Exploring barriers and enablers to implementation of cancer screening among primary care professionals seeing marginalized patients
Bibliographic record
Abstract
BACKGROUND: Cancer screening is an important prevention tool shown to improve cancer morbidity and mortality. Primary care professionals (PCPs) can play an important role in facilitating cancer screening and addressing barriers. Our aim was to learn from PCPs that see a high proportion of patients experiencing marginalization and that have high screening rates in their practices (high performers) to identify key barriers and enablers to addressing cancer screening with this patient population. METHODS: This was a qualitative descriptive study conducted using the principles of 'design thinking' to engage PCPs who are high performers in order to understand key barriers and enablers to cancer screening. An interview guide informed by the Systems Model of Clinical Preventive Care was used to collect data. Participants eligible for this study included both physicians and nurse practitioners working in Ontario in a variety of settings including solo and team-based practice models. All interviews were audio-recorded, transcribed verbatim and checked for quality assurance. Transcripts were coded by two independent members of the research team using deductive content analysis. The data were mapped onto the Systems Model of Clinical Preventive Care domains and presented in a narrative summary. RESULTS: We interviewed a total of 22 PCPs of which 54.5% were women and just over half (54.5%) were White. Most participants worked in a team-based primary care model. Our results suggest that a number of strategies can support high screening rates among those experiencing marginalization including interprofessional team-based collaborative practice, culturally competent and trauma-informed care, and adaptive approaches to overcome barriers such as improving the ease, access, and acceptability of the screening test. CONCLUSION: Addressing cancer screening with patients experiencing marginalization requires a multi-pronged approach to care to facilitate screening. Team-based models of care may have more infrastructure and supports in place to support PCPs in addressing cancer screening with patients experiencing marginalization. Lastly, providers and teams need to work in a supportive clinical context that allows for innovation to address system barriers to promote and enable screening for those who are structurally marginalized.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".