Primary Care Networks: Alberta's Primary Care Experiment Is a Success – Now What?
Bibliographic record
Abstract
he 2009 H1N1 pandemic vaccination campaign was an excellent opportunity to witness the many benefits of collecting individual-level immunization data at the point of vaccination.Most provinces and territories required the reporting of at least partial demographic vaccination data from their local public health agencies, so timely vaccine coverage data were available to inform operational planning and infection prevention activities.Many provincial and local public health agencies are now building on this momentum and are incorporating similar datacollection approaches into their seasonal influenza vaccination campaigns.However, our research has shown a disconcerting lack of uniformity across the country in the collecting and reporting of data elements.We have reviewed related literature and consulted with various provincial and territorial ministries and healthcare professional organizations, and our findings confirm that this issue is even more pronounced in non-public health settings, where provincial and territorial standards mandating the systematic collection and central reporting of immunization data from physicians' offices, hospitals, pharmacies and private clinics are either absent or not enforced.Further, where physicians report immunization data to their local public health agencies, these do not typically extend beyond the number of doses administered (Carolyn Sanford, Prince Edward Island's Department of Health and Wellness, personal communication, August 23, 2010).In jurisdictions where pharmacists have authorization to administer vaccines, immunization information (date, vaccine name/lot number) must be documented in each client's profile (Alberta College of Pharmacists 2009), but there are a lack of provincial standards around reporting to health authorities (Cheryl McIntyre, BC Centre for Disease Control, personal communication, April 19, 2011).In other pharmacy-based and workplace clinics across the country, privately funded vaccines are administered by nurses who report aggregate dose data to the employer and retain the client records (Nan Cleator, Victorian
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".