Desenvolvendo um modelo de revisão rápida para graduação em Educação Física
Bibliographic record
Abstract
OBJETIVO: Elaborar um modelo de revisão rápida que pode ser útil para trabalhos de conclusão de curso (TCC) de Educação Física, e para a aproximação entre evidências científicas e prática profissional. MÉTODOS: São descritas as adaptações feitas para criar um método alternativo de condução de revisões rápidas para TCC’s de curso de graduação. RESULTADOS: Aluna e professor apresentaram os dados extraídos dos estudos, opinião inicial e síntese final da aluna e, finalmente, relato crítico de membros da banca e pesquisadora com experiência em tradução de conhecimento (Knowledge Translation, KT) sobre a estratégia desenvolvida froam incluídos. CONCLUSÃO: Espera-se que esse relato propicie discussão continuada sobre o processo de orientação de TCC’s na Educação Física, neste caso com ênfase em Atividade Física e Saúde e formas de se fazer a evidência científica se aproximar da prática, através de ações direcionadas aos futuros profissionais. ABSTRACT. Developing a rapid review model for undergraduate level in Physical Education. OBJECTIVE: To elaborate on a rapid review model that can be useful for undergraduate thesis projects (TCC) in Physical Education courses, and for approximate science and professional practice. METHODS: We described the adaptations performed in order to come up with an alternative method to develop rapid reviews in fourth year projects at the undergraduate level. RESULTS: A student and professor presented extracted data from the studies, the student provided initial opinion and final synthesis and, finally, critical appraisal of the committee and researcher with expertise in knowledge translation about the developed method were included. CONCLUSION: We expect that this report will promote discussion on the topic about the supervision of fourth year research projects in Physical Education, with emphasis on Physical Activity and Health and offers a way to bridge the gap between scientific evidence and practices through actions directed at to future professionals.
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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 | 0.047 | 0.126 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".