Fear of COVID-19 as mediator in the relationship between at-risk Filipino women’s health belief and intention to have pap smear test
Bibliographic record
Abstract
Despite being one of the most preventable and curable types of cancer, cervical cancer still causes death among Filipino women yearly. The pandemic created new obstacles for women to overcome, but some of the obstacles that existed before COVID-19 may now be worsened and will seriously affect women's self-care management, limiting their access to the necessary procedures needed for the screening of cervical cancer. This study aimed to determine the relationship of at-risk Filipino women’s health beliefs towards their intention to have Pap Smear test and the mediating role of fear of COVID-19. A causal predictive approach was conducted and 572 female Filipino currently residing in the Philippines participated in the study. Data were gathered utilizing the Health Belief Model Scale for Cervical Cancer and the Pap Smear Test and the Fear of COVID-19 Scale with the Intention to Screen assessed by a structured question. With p-values of < .05 which is considered statistically significant in this study, the outcome of the mediation analysis shows that the mediated effect of fear of COVID-19 is not particularly (partial mediation) significant. There is still a significant direct association between health beliefs particularly on the benefits of Pap Smear test and health motivation and barriers to Pap Smear test with the intention to screen even without the presence of fear. In conclusion, the fear of COVID-19 plays a minor effect in the association between Filipino women's health beliefs and their intention to attend Pap smear testing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".