Clinicians' defences: An empirical study
Bibliographic record
Abstract
OBJECTIVE: Clinicians' defence mechanisms are strategies used to manage the stress and the negative affects emerging during a therapy session. The first objective of the study is to adapt the defence mechanisms rating scales (DMRS), originally created by Perry for assessing patient defences, in order to evaluate clinician defences. The second objective is to explore the type of defence mechanisms used by clinicians in oncology. The third objective is to study the sensitivity of the instrument by assessing changes in defensive functioning after specific communication skills training (CST) in oncology. DESIGN: Participants (N=20) were oncology clinicians participating in oncology CST. The defence mechanism rating scales for clinicians (DMRS-C) was used to assess the use of the clinicians' defences before and after CST. RESULTS: The instrument showed promising preliminary psychometric properties. Numerous and very varied defences were coded in each session and corresponding to a great variety of defences. After CST, the clinicians' overall defensive functioning (ODF) increased. Considering the defences' levels, a decrease in the use of immature defences was observed. CONCLUSIONS: Taking into consideration the importance of clinicians' variables in treatment outcome, this instrument constitutes a promising way of assessing the clinician's strategies used to face the emotional difficulties emerging during the therapeutic encounter.
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How this classification was reachedexpand
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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".