The social and economic impacts of cervical cancer on women and children in low‐ and middle‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND: There is limited knowledge on the social and economic impacts of a diagnosis of cervical cancer on women and children in low- and middle-income countries (LMICs). OBJECTIVES: To determine the social and economic impacts associated with cervical cancer among women and children living in LMICs. SEARCH STRATEGY: The MEDLINE, PsychInfo, CINAHL, Pais International, and CAB Global Health databases were systematically searched to retrieve studies up to June 2021. SELECTION CRITERIA: Studies were included if they reported on either the social or economic impacts of women or children in a LMIC. DATA COLLECTION AND ANALYSIS: Data was independently extracted by two co-authors. The authors performed a quality assessment on all included articles. MAIN RESULTS: In all, 53 studies were included in the final review. Social impacts identified included social support, education, and independence. Economic impacts included employment and financial security. No study reported the economic impact on children. Studies that utilized quantitative methods typically reported more positive results than those that utilized qualitative methods. CONCLUSIONS: Additional mixed-methods research is needed to further understand the social support needs of women with cervical cancer. Furthermore, research is needed on the impact of a mother's diagnosis of cervical cancer on her children.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".