Knowledge, attitudes and perceptions among non blood donor female health care professionals
Bibliographic record
Abstract
INTRODUCTION: Blood donation is necessary in order to maintain an adequate supply of blood to patients who are suffering from any kind of disease or trauma, which requires them to have blood transfusion. Female non-blood donors are generally low in number. Therefore, this research was carried out to assess the main reasons behind the lack of blood donations made by females, and their knowledge, attitude and perceptions towards voluntary blood donation. METHODOLOGY: A cross-sectional study was conducted on 664 female health professionals, who were selected by non-probability convenience sampling from two tertiary care hospitals. A pretested questionnaire was presented to the sample population, and the data was entered and analyzed on SPSS (V17). RESULTS: 94.6 % were aware with the fact that blood is screened for AIDS, Hepatitis B and C before transfusion. Moreover, 83.7% said that they will only donate blood if a family, relative or friend would need it and similarly 83.4% suggested that they would donate blood if blood donation camps are arranged in hospital premises. 81.8 % thought that blood donors can contract Hepatitis B after donation where as only 29.5% did not blood due already blood loss in menstrual cycle. CONCLUSION: The participants had adequate knowledge about the benefits of blood donation. The most important reason identified for not donating blood is the lack of facilities within the workplace or lack of approach by responsible authorities. The results of the study may help in minimizing the misconceptions of the participants about blood transfusion, which would increase their contribution towards blood donation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".