Canadian family physicians' and paediatricians' knowledge, attitudes and practices regarding A(H1N1) pandemic vaccine
Bibliographic record
Abstract
BACKGROUND: One of the main determinants of public immunization success is health professionals' support and recommendations. Little is known about the physicians' level of support and intentions regarding A(H1N1) pandemic influenza vaccination. The aim of this survey was to document Canadian family physicians' and paediatricians' knowledge, attitudes and practices (KAP) as well as their intentions regarding A(H1N1) pandemic influenza vaccines right before the beginning of the largest immunization campaign in Canadian history. FINDINGS: A self-administered, anonymous, mail-based questionnaire was sent to a random sample of family physicians and to all paediatricians practicing in Canada. All 921 questionnaires received by October 29 2009 were included in the analysis. Between 72% and 92% of respondents agreed with the statements regarding vaccine safety, effectiveness and acceptability. More than 75% of respondents intended to recommend the A(H1N1) pandemic influenza vaccine to their patients and to get vaccinated themselves. The most significant factors associated with the intention to recommend A(H1N1) pandemic vaccines were physicians' intention to be vaccinated against influenza themselves and the perceived acceptability of the vaccine by the vaccinators. CONCLUSIONS: Most Canadian family physicians and paediatricians surveyed were supportive of the A(H1N1) pandemic influenza vaccination before its implementation and large media coverage.
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 | 0.008 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".