MétaCan
Menu
Back to cohort
Record W1970132625 · doi:10.1097/pep.0b013e318218f2fe

A Description of Professional Pediatric Physical Therapy Education

2011· article· en· W1970132625 on OpenAlexaff
Joe Schreiber, Shelley Goodgold, Victoria A. Moerchen, Nushka Remec, Carolanne Aaron, Alison Kreger

Bibliographic record

VenuePediatric Physical Therapy · 2011
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsKruger (Canada)
Fundersnot available
KeywordsTask forceProfessional developmentMedical educationMedicineProfessional associationTask (project management)Work (physics)Family medicinePsychologyPhysical therapyPolitical sciencePublic relations

Abstract

fetched live from OpenAlex

In Brief Purpose: The purpose of this work was to reexamine the status of professional pediatric physical therapy education in the United States. Methods: A task force designed a 16-item survey and contacted representatives from all professional physical therapy programs. Results: Surveys were gathered from 151 programs for a return rate of 75%. Much variability exists across programs in total number of hours devoted to pediatrics (range, 35-210 hours). In addition, almost 60% of respondents indicated that the individual responsible for delivering the pediatric content will be retiring within the next 15 years. Conclusion: These results describe current pediatric professional education and provide numerous opportunities and challenges for the development of optimal professional pediatric education. Supplemental digital content is available for this articleThis report of a survey of professional educational programs provides a description of current pediatric professional education and reveals numerous opportunities and challenges for the development of optimal professional pediatric education.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.005

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.

Opus teacher head0.203
GPT teacher head0.470
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2011
Admission routes1
Has abstractyes

Explore more

Same venuePediatric Physical TherapySame topicOccupational Therapy Practice and ResearchFrench-language works237,207