Personal and Professional Immunization Behavior Among Alberta Chiropractors: A Secondary Analysis of Cross-Sectional Survey Data
Bibliographic record
Abstract
OBJECTIVES: This study examined the relationship among chiropractors' personal immunization decisions, the vaccination status of their children, and their interest in referring patients for immunization. METHODS: This was a secondary analysis of data collected in a 2002 postal survey of Alberta chiropractors (response rate, 78.2%). Analysis was restricted to chiropractors with children (n = 325). Chiropractors indicated their own vaccination status, that of their children, and their interest in referring patients for immunization. Data analysis included frequencies, cross tabulations, and logistic regression models (alpha = .05). RESULTS: Most respondents were male (83.4%), had more than one child (71.8%), and had graduated from chiropractic college a median of 13 years before survey. Of the chiropractors, 92.6% had ever been immunized, but only 35.7% would accept immunization for themselves in the future. Further, 66.8% had at least one immunized child, and 21.8% indicated interest in referring patients for immunization. Chiropractors who would accept immunization for self in the future, compared with those who would not, were more likely to indicate interest in patient referral for immunization (odds ratio, 11.4; 95% confidence interval, 5.4-24.0; P < .001). Chiropractors who have at least one immunized child, compared with those with none immunized, were 6.2 times more likely to indicate interest in referring patients for immunization (odds ratio, 6.2; 95% confidence interval, 1.4-28.4; P = .018). CONCLUSIONS: Alberta chiropractors are consistent in their personal and professional behaviors. Chiropractors who accept vaccinations for themselves or their children are more likely to refer patients to public health for immunizations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".