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Record W4406956566 · doi:10.1097/jte.0000000000000395

The Impact of Physical Therapy Postprofessional Education Programs on Productivity in a Large Academic Medical Center

2025· article· en· W4406956566 on OpenAlexaff
Mark D. Weber, Carol Jo Tichenor, Joseph Farrell, Melissa S. Kidder, Becky Olson-Kellogg, Craig P. Hensley, Kendra L Harrington, Matthew S. Briggs

Bibliographic record

VenueJournal of Physical Therapy Education · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsSpecialtyProductivityMedicineDescriptive statisticsBoard certificationCertificationFamily medicineCurriculumMedical educationResidency trainingPsychologyContinuing educationManagement

Abstract

fetched live from OpenAlex

INTRODUCTION: Emerging evidence shows positive impact of postprofessional physical therapy education (residency and fellowship) specific to participants; however, outcomes on organizational impact are largely unknown. The purpose of this project was to describe the impact residency and fellowship training has on financial metrics. A secondary purpose of this case study was to describe trends associated with higher productivity. REVIEW OF LITERATURE: Previous studies have demonstrated positive professional behavior and generalized operational impact of postprofessional education. No studies have evaluated the impact of residency and fellowship training on individual physical therapist (PT) productivity. SUBJECTS: Individual productivity from 191 PTs was collected over a 10-year time frame from a large, ambulatory, rehabilitation department within an academic medical center. Productivity was compared between 4 groups: nonresidency- or fellowship-trained clinicians; residency-trained clinicians; fellowship-trained clinicians; and dual residency and fellowship-trained clinicians. METHODS: Physical therapists' productivity data were manually extracted retrospectively from operational reports over a 10-year period. Additional data elements extracted included the following: board specialty certification and years employed at the medical center. Data were then categorized as nonresidency/fellowship -trained, residency trained, fellowship trained, or dual residency and fellowship trained. Data were analyzed using descriptive statistics and 1-way analysis of variance (ANOVA). RESULTS: Forty-five clinicians with postprofessional training collectively produced $253,617 more in financial annual return to the organization. Fellowship-trained clinicians demonstrated the highest productivity followed by residency-trained and dual residency and fellowship-trained clinicians. Specialty board certification also positively increased productivity regardless of postprofessional training. DISCUSSION AND CONCLUSION: Postprofessional training within physical therapy continues to be evaluated in the spectrum of professional development. Evidence supports positive professional behaviors and patient outcomes; however, little is known regarding its impact on productivity metrics. Although no significance was found between the groups with and without postprofessional training, meaningful financial return was demonstrated in clinicians with postprofessional training. The lack of significance may have been influenced by compression because of departmental productivity guidelines. This preliminary data may assist organizations in justifying resources for sustaining and sponsoring future programs.

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)0.004
metaresearch head score (Gemma)0.020
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.074
GPT teacher head0.569
Teacher spread0.495 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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