Using implementation science to engage stakeholders and improve outcome measurement in a preschool speech-language service system
Bibliographic record
Abstract
Background: This tutorial presents one example of collaborative implementation research in a preschool speech-language service system – Ontario Canada's Preschool Speech and Language Program. Working collaboratively with stakeholders including policy makers, managers, and speech-language pathologists (SLPs), four webinar modules were developed to support implementation of the Focus on the Outcomes of Communication Under Six (FOCUS), a new participation-focused outcome measurement tool in pediatric speech-language pathology. The webinar modules were pilot tested at two community sites to determine whether they were effective at increasing SLPs’ knowledge, perceptions, and intentions for practice. The Knowledge-to-Action framework was used to inform all phases of this work. Methods: Forty-six SLPs completed an initial 15-item survey online, consecutively viewed the four webinar modules (67 minutes), and then completed a final 15-item survey online. Results: After viewing the webinar modules, SLPs reported significantly higher perceptions about the value of participation-based outcome measures and outcome monitoring; perceptions of reliability, validity and clinical utility of the FOCUS; intentions to use data from the FOCUS to support clinical discussions and decision making; and intentions to submit data as part of a provincial outcome monitoring program. Conclusions: Barriers to this type of implementation research included a variety of challenges related to methodology. Facilitators included research products that were highly relevant to the practice context, high rates of participation in our pilot study, and external validity for pilot study results. Collaborating with stakeholders is an important part of implementation work and is critical for ensuring research is relevant to and applicable in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".