Incorporating client perspectives: moving towards digital outcome measurement in pediatric speech-language pathology
Bibliographic record
Abstract
Caregivers’ needs and preferences regarding outcome measurement in pediatric speech-language pathology are not well-understood, but are critical to the development and implementation of meaningful clinical tools. This project engaged caregivers of preschoolers with speech, language and communication needs to understand their views on the potential for, and their preferences surrounding a digital version of one participation-focused outcome measure called the Focus on the Outcomes of Communication Under Six (FOCUS-34). Fifteen caregivers of preschoolers who were receiving services in a large health system participated in one of four 30–60-minute virtual focus groups or one of three individual interviews. Caregivers shared their perceptions of whether and how a digital FOCUS-34 may improve their service experience, and their preferred features and formats to make it useful. An inductive content analysis was used to identify relevant categories that described caregivers’ perspectives. Data were sorted into two categories: (1) caregivers believe a digital solution would improve their service experience, and (2) caregivers want a user-friendly digital FOCUS-34 to measure and give feedback on intervention outcomes. Multiple sub-categories were also identified, which further described caregivers’ views on how a digital measure would improve the feasibility of outcome measurement, family engagement in services, and transparent communication with providers. Sub-categories also outlined caregivers’ preferences for the features and functions of a digital measure and their suggested considerations for developers of the digital tool. Results provide new insight into caregivers’ perspectives on digital outcome measurement and will inform efforts to improve the utility of the FOCUS-34 for families.
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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 | 0.134 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| 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".