Implicit Bias and the Feedback Paradox: Exploring How Health Professionals Engage With Feedback While Questioning Its Credibility
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Learners and practicing health professionals may dismiss emotionally charged feedback related to self, yet little research has examined how to address feedback that threatens an individual's identity. The implicit association test (IAT) provides feedback to individuals regarding their implicit biases. Anticipating feedback about implicit bias might be emotionally charged for mental health professionals, this study explored their experience of taking the IAT and receiving their results, to better understand the challenges of identity-threatening feedback. METHOD: The researchers sampled 32 psychiatry nurses, psychiatrists, and psychiatric residents at Western University in Ontario, Canada, after they completed the mental illness IAT and received their results. Using constructivist grounded theory, semistructured interviews were conducted from April to October 2017 regarding participants' experience of taking the IAT. Using constant comparative analysis, transcripts were iteratively coded and analyzed for results. RESULTS: While most participants critiqued the IAT and questioned its credibility, many also described the experience of receiving feedback about their implicit biases as positive or neutral. Most justified their implicit biases while acknowledging the need to better manage them. CONCLUSIONS: These findings highlight a feedback paradox, calling into question assumptions regarding self-related feedback. Participants' reactions to the IAT suggest that potentially threatening self-related feedback may still be useful to participants who question its credibility. Further exploration of how the feedback conversation influences engagement with self-related feedback is needed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.047 | 0.108 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it