Challenges and solutions in speech-language pathology across Australia and Canada
Bibliographic record
Abstract
Background, aims and objectives: This study aimed to compare the perception of barriers to service delivery among speech-language pathologists (SLPs) in Canada and Australia and the extent to which they used parent or carer training to overcome these barriers. Methods: Participants were 81 Australian and 63 Canadian SLPs who completed an online survey. Questions comprised open ended and forced choice questions with some ranking of questions also required. Chi-square analyses were conducted comparing Canadian and Australian SLPs. Results: Few differences existed among the respondents. Respondents overwhelmingly selected “not enough speech-language pathology positions to meet demand” as their main barrier. This barrier along with “parents/carer’s lack of knowledge about the need for speech-language pathology”, “lack of parent/carer engagement” and “lack of awareness of role of speech-language pathologist” were the principal barriers. Training parents and carers to conduct therapy at home was the most used strategy among both Canadian and Australian SLPs. Discussion: The finding that the SLPs perceive low engagement from parents both in the training sessions and when working with their child may suggest that there is a need for speech-language pathologists to determine more effective ways to train and engage parents and carers. Conclusion: More research into the efficacy of parent or carer training across a wider range of speech-language pathology practice areas and across a more diverse range of parents or carers needs to be undertaken.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".