Engaging clinical end users in the development of an outcome measurement protocol for paediatric communicative health systems
Bibliographic record
Abstract
PURPOSE: To develop a conceptual framework of the factors likely to influence clinicians' use of a new participation-focused outcome measurement protocol in a large paediatric speech-language pathology program. METHOD: A convenience sample of 27 end users (clinicians, managers) were recruited from Ontario, Canada's Preschool Speech and Language Program. Participants engaged in one virtual concept mapping session in groups of five to six during which they learned about the new protocol, and generated statements in response to a prompt asking them to identify factors that would influence their use of the protocol. Following all sessions, participants asynchronously sorted and rated all statements, and data were analysed using multidimensional scaling and hierarchical cluster analyses. RESULT: Six themes were identified: (1) response from families; (2) use of resources; (3) feasibility and clinical utility; (4) relevance and value-added for clinicians; (5) streamlining policies and guidelines; and (6) delivery, administration, and modification of tool. Response from families, feasibility and clinical utility, and use of resources received the highest importance ratings. CONCLUSION: Concept mapping methodology was used to engage clinicians and managers to identify the barriers to a new implementation protocol for outcome measurement. Results will support future research and implementation efforts.
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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 | 0.623 | 0.586 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".