Lessons learned in practice-based research: Studying language interventions for young children in the real world
Bibliographic record
Abstract
Background and aims Practice-based research holds potential as a promising solution to closing the research-practice gap, because it addresses research questions based on problems that arise in clinical practice and tests whether systems and interventions are effective and sustainable in a clinical setting. One type of practice-based research involves capturing practice by collecting evidence within clinical settings to evaluate the effectiveness of current practices. Here, we describe our collaboration between researchers and clinicians that sought to answer clinician-driven questions about community-based language interventions for young children (Are our interventions effective? What predicts response to our interventions?) and to address questions about the characteristics, strengths, and challenges of engaging in practice-based research. Methods We performed a retrospective chart review of 59 young children who had participated in three group language interventions at one publicly funded community clinic between 2012 and 2017. Change on the Focus on the Outcomes of Communication Under Six (FOCUS), a government mandated communicative participation measure, was extracted as the main outcome measure. Potential predictors of growth during intervention were also extracted from the charts, including type of intervention received, attendance, age at the start of intervention, functional communication ability pre-intervention, and time between pre- and post-intervention FOCUS scores. Results Overall, 49% of children demonstrated meaningful clinical change on the FOCUS after their participation in the language groups. Only 3% of participants showed possibly meaningful clinical change, while the remaining 46% of participants demonstrated not likely meaningful clinical change. There were no significant predictors of communicative participation growth during intervention. Conclusions Using a practice-based research approach aimed at capturing current practice, we were able to answer questions about the effectiveness of interventions delivered in real-world settings and learn about factors that do not appear to influence growth during these interventions. We also learned about benefits associated with engaging in practice-based research, including high clinical motivation, high external validity, and minimal time/cost investment. Challenges identified were helpful in informing our future efforts to examine other possible predictors through development of a new, clinically feasible checklist, and to pursue methods for improving collection of outcome data in the clinical setting. Implications: Clinicians and researchers can successfully collaborate to answer clinically informed research questions while considering realistic clinical practice and using research-informed methods and principles. Practice-based research partnerships between researchers and clinicians are both valuable and feasible.
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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 | 0.117 | 0.198 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".