Reflecting on Reflection in Clinical Social Work: Unsettling a Key Social Work Strategy
Bibliographic record
Abstract
Abstract This study calls into question some of assumptions within social work education and practice regarding the transformational potential of reflection-on-practice. Participants (n = 34) in this simulation-based study each engaged in two clinically focused simulations, each followed by an interview which included observing segments of their video-recorded simulations. The objectives of this study are to make some sense of the misalignments between participants’ post-simulation reflections of their practice behaviours, and the practice behaviours observed by the research team, and later by participants themselves. Findings illustrate that: (i) how clients and the session are understood in the moment can be different than how they are understood post-client engagement; (ii) reflections of practice behaviours can be tied to how individuals want to be, which are not necessarily aligned with how they are in the moment; and (iii) reflections are intertwined with awareness of oneself. These results identify some of the limitations of individualised reflective activities, and demonstrate the powerful potential of collective reflection on recordings of simulations to challenge memory distortions, retrospective biases and to promote greater accountability to ourselves as social workers, and to those we work with.
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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 | 0.116 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.058 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".