Speech-Language Pathologists in Pediatric Palliative Care: An International Study of Perceptions and Experiences
Bibliographic record
Abstract
Purpose The purpose of this study is to gather qualitative data regarding the experiences and perceptions of speech-language pathologists (SLPs) practicing in pediatric palliative care (PPC) and investigate their approaches for working with patients, families, and the multidisciplinary team. Method Ten participants from Australia, Canada, and the United States were recruited from a previous survey study. In-depth interviews were conducted via videoconferencing. Two coders used NVivo software to record and organize the data into themes. Interrater reliability of the coding was established with a third coder. The COnsolidated criteria for REporting Qualitative research 32-item checklist has been used for reporting the research. Results Interrater coding reliability (using weighted kappa) was .81, indicating "almost perfect" agreement on the selection of themes from the code frame. Emerging themes included clinical interventions, unique SLP skills in PPC, multidisciplinary team relationships, workforce issues, education and training, governance, medicolegal considerations, bioethical issues, and holistic care. Additional subthemes are also noted. In the absence of research evidence and practice recommendations, SLPs rely on client perspectives and clinical experience to guide their professional practice in PPC. Conclusions Qualitative survey analysis of the perceptions and experiences of SLP clinicians from three postindustrial countries identified nine major themes regarding PPC. Results expand on limited literature to date in the area of PPC. Future research using larger samples is needed to provide clinicians with best practice recommendations for working with patients, families, and the broader care team.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".