Understanding Acceptability, Barriers, and Facilitators to Clinical Implementation of the on Track Developmental Monitoring System for Children with Cerebral Palsy: A Qualitative Study
Bibliographic record
Abstract
AIMS: On Track Developmental Monitoring System (DMS) is a novel series of tools to assist in shared-decision making, guide rehabilitation intervention based on functional ability levels, and promote episodic care service models. Further understanding of the acceptability, feasibility, and appropriateness of On Track DMS in clinical settings is critical. The purpose of this study was to understand clinician perspectives of the acceptability of On Track DMS and to identify potential implementation barriers and facilitators within pediatric physical therapist practice. METHODS: Three, day-long training workshops were conducted with 32 pediatric physical therapists across the US. Focus groups with 21 workshop participants were conducted following training. Results were audio recorded, transcribed verbatim, and coded into themes. RESULTS: . CONCLUSIONS: On Track DMS appears to have initial value and acceptability for pediatric physical therapists across practice settings. Perceived benefits include facilitation of data-driven practice and therapist/family collaboration to improve health outcomes for children with CP. Using this data to understand and assess barriers and facilitators to knowledge use are first steps in successfully implementing On Track DMS.
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 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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".