Physician perspectives on vaccination and diagnostic testing in children with gastroenteritis: A primary care physician survey
Bibliographic record
Abstract
OBJECTIVES: Gastroenteritis remains a common paediatric illness. Little is known about physician knowledge of enteric pathogen diagnostic tests. At the time of study conduct, Alberta lacked a publicly funded rotavirus vaccination program and knowledge of primary care physician perspectives was lacking. We sought to ascertain diagnostic testing methods and to understand knowledge and perceptions regarding enteric pathogen vaccination. METHODS: A 30-item electronic survey was distributed across Alberta's five health care zones. The survey was developed by virology, microbiology, paediatrics, family medicine and public health experts. Participants were members of Alberta's Primary Care Networks, the TARRANT network and The Society of General Pediatricians of Greater Edmonton. Study outcomes included: (1) physician knowledge of available diagnostic tests, (2) perspectives regarding stool sample collection and (3) support for an enteric vaccine program. RESULTS: (67%). Although electron microscopy and enzyme immunoassay were used to identify viruses in Alberta during the study period, only 20% and 48% of respondents respectively identified them as tests employed for such purposes. Stool testing was viewed as being inconvenient (62%; 55/89), whereas rectal swabs were thought to have the potential to significantly improve specimen collection rates (82%; 72/88). Seventy-three per cent (66/90) of the respondent physicians support the adoption of future enteric pathogen vaccines. CONCLUSIONS: Simplification of diagnostic testing and stool sample collection could contribute to improved pathogen identification rates. Implementation of an enteric vaccine into the routine paediatric vaccination schedule is supported by the majority of respondents.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".