Barriärer och främjande faktorer för kvinnliga migranters deltagande i screening för livmoderhalscancer : En litteraturöversikt över kvalitativ forskning
Bibliographic record
Abstract
Introduction: Globally approximately 570,000 women were diagnosed with cervical cancer during the year 2018 making the condition the fourth most common cancer in women. Studies indicate that migrant women are screened for cervical cancer to a lower extent than nonmigrant women, which is a problem that requires attention. Aim: The aim of the present study was to describe migrants' experiences of cervical cancer screening. The aim was specified with two questions: What do migrant women experience as hindering regarding screening for cervical cancer? What do migrant women experience as facilitating regarding screening for cervical cancer? Method: A descriptive design with a literature review was used, twelve scientific qualitative articles were reviewed and analysed. Results: The four main themes were: barriers related to the healthcare system, sociocultural barriers, barrier at the individual level, and factors that promote participation in cervical cancer screening. The results showed that the main barriers were insufficient knowledge, language barriers and cultural barriers. Facilitating factors included cultural adaption and increased dissemination of information. Conclusion: In order to also suit immigrant women development of the screening programmes in the countries included in the bachelor thesis (Sweden, Norway, Finland, USA and Canada) is needed.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.006 |
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".