Public Attitudes toward Cancer and Cancer Patients: A Jordanian National Online Survey
Bibliographic record
Abstract
Background: Public awareness and attitudes toward cancer and cancer patients are highly important in enhancing the effectiveness of cancer screening and early diagnosis programmes. This study aimed to explore the public attitudes toward cancer and cancer patients in Jordan. Method: A cross-sectional design was used to conduct this online survey study in Jordan between March 20th and April 20th 2020.The sample was conveniently selected, and 1157 participants were included from the public. The Public Attitudes toward Cancer Questionnaire was employed. Results: Descriptive statistics, unpaired t-test, ANOVA, and multiple linear regression were utilized. The mean age was 44.2 years (SD = 20.1), and 53% were female. The total mean attitude score was 38.2 (SD = 4.3). Based on the results, having a family member or a friend with cancer (P = 0.003), willingness to be informed about cancer diagnosis (P = 0.001), informing a friend about cancer diagnosis (P = 0.021), and willingness to participate in screening and early detection programmes (P < 0.001) were significant predictive positive attitudes towards cancer and cancer patients. In addition, being married predicted more negative attitudes compared with being single (P = 0.001). Conclusion: This study demonstrated that Jordanians had positive attitudes toward cancer and cancer patients and most were willing to be informed about cancer diagnosis. This calls for healthcare providers to adopt shared decision models when devising health care plans for cancer patients, with more involvement on the parts of both patients and family members rather than adopting a paternal approach. Policy makers and managers should consider positive attitudes when developing healthcare programmes to enhance public participation in early cancer detection and screening programmes so a s to reduce cancer mortality and morbidity rates.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".