Advancing the Evidence Base of Pediatric Physical Therapy: “Sincerely, From the Heart”
Bibliographic record
Abstract
PURPOSE: To share reflections on themes that have become important to me as a developing pediatric physical therapy researcher contributing to knowledge generation in our field. KEY POINTS: The themes are: select strong mentors, assemble a research team with diverse perspectives and strengths, be family-centered, be rigorous in approaches to study design to match the research question, mentor the next generation, and engage in knowledge translation and exchange. CONCLUSIONS: I encourage everyone to reflect on who has been or could be your mentors or mentees, and on how you can be a part of an effective, family-centered research team, by encouraging a breadth of research designs to answer the many research questions that remain and by assisting in the implementation of knowledge to practice. CLINICAL RELEVANCE: Outcomes for children and families receiving pediatric physical therapy services are enhanced through the use of evidence-based knowledge.
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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 | 0.153 | 0.378 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.019 | 0.028 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.016 | 0.047 |
| Insufficient payload (model declined to judge) | 0.004 | 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".