Knowledge, Attitude, Awareness, and Barriers Toward Influenza Vaccination Among Medical Doctors at Tertiary Care Health Settings in Peshawar, Pakistan–A Cross-Sectional Study
Bibliographic record
Abstract
Objective: This study aimed to investigate the knowledge, attitude and awareness of medical doctors towards influenza vaccination and the reasons for not getting vaccinated. Methods: A cross-sectional study was carried out among medical doctors in three major tertiary care hospitals in Peshawar, Khyber Pakhtunkhwa (KP), Pakistan. A web-based, pre-tested questionnaire was used for data collection. Results: A total of (n=300) medical doctors were invited, however only (n=215) participated in the study with a response rate of 71.7%. Among the participants, 95.3% (n=205) were males with a mean age of 28.67±3.89 years. By designation, 121(56.3%) were trainee medical officers and 40 (18.6%) were house officers. The majority had a job experience of 1-2 years (n=102, 60.6%). Of the total sample, 38 (17.7%) doctors reported having received some kind of vaccination, whereas only 19 (8.84%) were vaccinated against influenza. The results identified that the major barriers towards influenza vaccinations included (1) a general perception among the health-care professionals that influenza is not a serious condition and therefore not worth vaccinating against (relative importance index RII=0.775), (2) the non-compulsory requirement for vaccination (RII=0.756), (3) fear of needles (RII=0.713) and (4) the associated side-effects or safety concerns (RII=0.681). Additionally, 156(72.6%) of doctors were not aware of the influenza immunization guidelines published by the Advisory Committee on Immunization Practices (ACIP) and Centre for Disease Control (CDC). Physicians obtained a high score (8.27 ± 1.61) of knowledge and understanding regarding influenza and its vaccination followed by medical officers (8.06 ±1.37). Regression analysis revealed that gender was significantly associated with the knowledge score with males having a higher score (8.0+1.39) than females (6.80+1.61). Conclusion: A very low proportion of doctors were vaccinated against influenza, despite the published guidelines and recommendations. Strategies that address multiple aspects like increasing awareness and the importance of the influenza vaccine, the international recommendations and enhancing access and availability of the vaccine are needed to improve its coverage and health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".