Reflective processes of practitioners in head and neck cancer rehabilitation: a grounded theory study
Bibliographic record
Abstract
PURPOSE: This study systematically examined how experienced Speech-Language Pathologists (SLPs) use the processes of reflection to develop knowledge relevant for practice in the context of head and neck cancer (HNC) rehabilitation. METHOD: In-depth, semi-structured interviews were conducted with 12 SLPs working in HNC rehabilitation in North America. Grounded theory methodology was adopted for data collection and analysis. RESULT: The findings inform a preliminary reflective practice model that depicts the processes of reflection used by practitioners interviewed. Nine categories of reflective processes were identified by participant SLPs in terms of the processes of reflection: ongoing questioning, experimenting through trial and error, integrating knowledge from past cases, embracing surprise, thinking out of the box, being in the moment, consulting with colleagues, putting oneself in the patients' shoes, and discerning ethical issues. These findings provide empirical evidence that supports Schön's theory of reflective practice and contribute to knowledge about the ways in which SLPs use processes of reflection in the context of HNC rehabilitation. CONCLUSION: The findings of this study have implications for how SLPs perceive and consider their role as knowledge-users and knowledge producers in their day-to-day clinical work, as well as for building capacity for reflective practice.
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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 | 0.055 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".