Comparing two types of perspective taking as strategies for detecting distress amongst parents of children with cancer: A randomised trial
Bibliographic record
Abstract
OBJECTIVE: To compare two perspective taking strategies on (i) clinicians' ability to accurately identify negative thoughts and feelings of parents of children with cancer, and (ii) clinician distress. METHODS: Sixty-three hematology-oncology professionals and nursing students watched a video featuring parents of children with cancer. Participants were randomly assigned to one of two groups. In the imagine-self group, they were instructed to imagine the feelings and life consequences which they would experience if they were in the parents' position. In the imagine-other group, they were instructed to imagine the feelings and life consequences experienced by the parents. Parent-clinician agreement on thoughts/feelings was evaluated (standard stimulus paradigm). Clinician distress was also assessed. RESULTS: The intervention was effective in manipulating perspective type. The groups did not significantly differ on parent-clinician agreement. Concentrating on personal feelings (imagine-self strategy) did predict lower agreement when controlling for trait empathy. Clinician distress was higher in the imagine-self group. CONCLUSION: Although the link between perspective type and detection of distress remains unclear, the results suggest that clinicians who highly focus on their own feelings tend to be less accurate on parental distress and experience more distress themselves. PRACTICE IMPLICATIONS: This research could potentially improve communication training and burnout prevention.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".