Lessons from the team care playbook: Recommendations for COVID-19 vaccination dissemination and uptake.
Bibliographic record
Abstract
For too many of us, the implications of a worldwide pandemic unfolded in graduated stages of understanding that seemed too sluggish for the opponent we faced. For too many of us, even those of us in health care, the unfolding was terrifying; we felt blindsided and unprepared. If coronavirus disease 2019 (COVID-19) were a bully picking a fight, they got the first punch in before we even raised a fist. Now, many rounds in, health care teams will have an opportunity to deploy a new weapon against COVID-19. Vaccines are coming. We do not know when, who will pay for them, or the logistical aspects (e.g., storage and administration). We do not even know how effective they will be. Moreover, we must plan for mass vaccination in a chaotic and politically charged context that bears little resemblance to the ones with which we have experience. Nevertheless, in this fight, vaccines could be our winning blow. We are getting better at working with unknowns and in disrupted environments during COVID-19. We have some time to prepare, and we have some extant knowledge and experience in vaccine distribution and uptake. Health care teams can use these to best their adversary, and we can and should begin now. The aim of this article is to discuss how to mobilize interprofessional teams within systems of care to engage best practices in vaccine dissemination and uptake in the unique COVID-19 context. We begin by discussing challenges to dissemination and uptake and then provide solutions using our experiences in the primary care system. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.034 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.009 | 0.014 |
| Research integrity | 0.024 | 0.029 |
| Insufficient payload (model declined to judge) | 0.037 | 0.023 |
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".