Reflective practice in speech-language pathology: A scoping review
Bibliographic record
Abstract
PURPOSE: Within the profession of speech-language pathology, there is limited information related to both conceptual and empirical perspectives of reflective practice. This review considers the key concepts and approaches to reflection and reflective practice that have been published in the speech-language pathology literature in order to identify potential research gaps. METHOD: A scoping review was conducted using Arksey and O'Malley's (2005) framework. RESULT: A total of 42 relevant publications were selected for review. The resulting literature mapping revealed that scholarship on reflection and reflective practice in speech-language pathology is limited. Our conceptual mapping pointed to the use of both multiple and generic terms and a lack of conceptual clarity about reflection and reflective practice in speech-language pathology. Two predominant approaches to reflection and reflective practice were identified: written reflection and reflective discussion. Both educational and clinical practice contexts were associated with reflection and reflective practice. Publications reviewed were primarily concerned with reflection and reflective practice by novices and expert practitioners. CONCLUSION: Based on this review, we posit that there is considerable need for conceptual and empirical work with a goal to support university- and work-based educational initiatives involving reflection and reflective practice in speech-language pathology.
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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 | 0.027 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".