Outcomes of a Community of Practice on Quebec Speech Language Pathologists’ Voice Assessment Practices and Professional Identity
Bibliographic record
Abstract
In a context where different protocols for recommended practices in clinical voice assessment exist, while there are gaps in the literature regarding the evidence base supporting assessment procedures and measures, clinicians from regions where a strong community holding expertise in clinical and scientific voice practices lack can struggle to confidently develop their voice assessment practices. In an effort to improve voice assessment practices and strengthen professional identity among speech-language pathologists in Quebec, Canada, a community of practice (CoP) was established, with the aim of promoting knowledge sharing, implementing change in clinical practice, and improving professional identity. Thirty-nine participants took part in the CoP activities conducted over a four-month period, including virtual meetings and in-person workshops. Participants had a high rate of attendance (> 74% participation rate in virtual meetings), and were highly satisfied with their participation and intended to remain involved after the project’s end. Statistically significant changes in voice assessment practices were observed post-CoP, regarding probability of performing assessments (p < .001), and perceived importance of assessment for evaluative purposes (p <.001), as well as improvements in assessment specific confidence, specifically for procedure of auditory-perceptual assessment (p < .001) and purpose of aerodynamic assessment (p = .05). Moreover, there was an increase in professional identity post-CoP (p < .001) and participants felt they made significant learnings. The present study highlighted the need to involve SLPs in future research to identify assessments that are relevant to the specific evaluative objectives of SLPs working with voice, and suggests CoPs are an efficient tool for that purpose.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".