Palliative Care Training for Medical Speech-Language Pathologists: A Multiple-Baseline Design
Bibliographic record
Abstract
PURPOSE: Current research supports favorable outcomes using online continuing education, and speech-language pathologists (SLPs) report a lack of training in palliative care. This study aimed to determine the effectiveness of online palliative care training on the knowledge and comfort level of medical SLPs. METHOD: In the multiple-baseline across participants method, 10 medical SLPs completed online training modules provided by the Center to Advance Palliative Care. An electronic visual analog scale was used to collect knowledge and comfort ratings. Seven intervention modules were completed asynchronously with self-perceived knowledge and comfort measured following each session. A follow-up phase was used to determine whether the gains were maintained for 3 weeks after the intervention. RESULTS: Nine of 10 participants experienced statistically significant improvements in knowledge, which were maintained through the follow-up phase. Eight of 10 participants demonstrated statistically significant improvements in comfort, which were maintained through the follow-up phase. CONCLUSIONS: To date, no other study has examined the effects of online palliative care training on medical SLPs. This investigation provides evidence that online, asynchronous continuing education for medical SLPs may improve their self-perceived knowledge and comfort in palliative care. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.27964515.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".