Uncovering motivators and stumbling blocks: Exploring the clinical research experiences of speech-language pathologists
Bibliographic record
Abstract
PURPOSE: Healthcare providers increasingly expect that allied health staff will not only translate research evidence into their clinical practice, but also generate research. Little is known about how well prepared clinicians are to meet these expectations. Research suggests that allied health professionals, including speech-language pathologists, have moderate levels of interest in research, but only little-to-moderate experience participating in research activities. The present study aimed to explore the experiences and attitudes of speech-language pathologists in regards to undertaking research in their clinical settings. METHOD: Focus groups were conducted with 21 practising speech-language pathologists (13 research engaged, eight not research engaged). The focus groups were transcribed and the data analysed qualitatively using content analysis. RESULT: Two overarching themes mediated research engagement. Engagement in research was shaped by whether participants overcame any "fear" of research and the unique characteristics of their clinical context. Contextualizing and further shaping participants' experiences of these themes were personal factors, such as initiative and proactivity. CONCLUSION: The success of increasing the research engagement of the speech-language pathology workforce may be contingent on providing clinicians with more exposure to research opportunities and mentors as well as ensuring organizational structures are in place to encourage, support and facilitate research.
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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 | 0.065 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".