Social capital interventions for human papillomavirus (HPV) immunization and cervical cancer screening: A rapid review
Bibliographic record
Abstract
Background: Social capital can be used as a conceptual framework to include social context as a predictor of human papillomavirus (HPV) vaccination and cervical cancer screening behaviours. However, the effectiveness of interventions that use social capital as a mechanism to improve uptake of immunization and screening remains elusive. Objective: To synthesize empirical evidence on the impact of social capital interventions on HPV immunization and cervical cancer screening and describe key characteristics of such interventions. Methods: Using a rapid review methodology, a search of literature published between 2012 and 2022 was conducted in four databases. Two researchers assessed the studies according to inclusion criteria in a three-step screening process. Studies were assessed for quality and data concerning social capital and equity components and intervention impact were extracted and analyzed using narrative synthesis. Results: Seven studies met the inclusion criteria. Studies found improved knowledge, beliefs and intentions regarding HPV immunization and cervical cancer screening. None of the studies improved uptake of immunization; however, three studies found post-intervention improvements in uptake of cervical cancer screening. All studies either tailored their interventions to meet the needs of specific groups or described results for specific disadvantaged groups. Conclusion: Limited evidence suggests that interventions that consider and reflect local context through social capital may be more likely to increase the uptake of HPV immunization and cervical cancer screening. However, further research must be done to bridge the gap in translating improvements in knowledge and intention into HPV immunization and cervical cancer screening behaviours.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".