The Future of Preventative Medicine: Health Promotion Programs as a Tool to Reduce Administrative Costs and Improve Health Outcomes - Case Study: Encouraging Vaccination Confidence Outreach Project
Bibliographic record
Abstract
Patient-centered care evolves around proper coordination between healthcare providers in consultation with patient needs. Oftentimes, children play a passive role in their health care because of the controversy around age and lack of consensual abilities. The impact of engaging children early on to empower decision-making skills has been proven to influence their ability to make informed and knowledgeable decisions. Consequently, if not provided with support, a child can make unwise decisions in the realm of their healthcare. In academia, children may develop the skills needed to make informed decisions with the adaption of inquiry-based and discovery learning methodologies. Particularly, science educators emphasize investigative skills using micro-science in the public-school curriculum. Generally, healthcare providers have relied on the traditional approach of intervention when treating conditions instead of engaging children early on with preventative approaches like the utilization of health promotion programs. As a result, healthcare systems are extremely strained due to higher rates of preventable conditions across Canada and the United States. This literature review examines recent research on 1) inquiry-based learning, 2) micro-science outreach and 3) health promotion programs linked to the reduction of healthcare costs. Next, a case study of Dalhousie University’s COVID-19 Vaccine Hesitancy Outreach Project is dissected to see its impact on children’s overall scientific comprehension and health care decision-making procession. Subsequently, the exploration of micro-science combined with inquiry-based learning as a psychological tool to increase cognitive agility is debated. Upon conclusion, the synthesizes of recent research led to a proposal of how a healthcare administrator could utilize micro-science in health promotion programs to lower healthcare costs.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".