Look and Listen: How the Coldness of Health Care Providers is Modifiable
Bibliographic record
Abstract
Purpose: To explore how we can make Health Care Providers (HCP) more conscious of their gaze and to encourage HCPs to make more eye contact with their patients, making them better suited to promote meaningful lives for them, thus strengthening the patient-provider relationship. Methods: Mixed quantitative and qualitative descriptive experimental design with narrative data analysis. 40 participants (23 HCPs and 17 chronic pain patients) viewed standardized videos depicting a patient-provider interaction in which the HCP did not look at the patient. Self-assessments and reflections were obtained. Results: Most HCPs recognized the clinical approach in the videos as cold, whereas 41% of patients recognized it as “normal”. When looking into patient’s eyes, 44% of HCPs were unable to identify the patients’ emotions, nor their own feelings. Powerlessness and vulnerability were emotions often felt by the HCP. Patients and HCPs agree that better addressing meaningful activities in a patient’s life, as well as looking at the patient more, would positively impact patient outcomes and pain management. At the one-month follow-up, 74% of HCPs had increased the amount of eye-contact made during their encounters and paid more attention to the relational aspect of their care. Conclusion: We succeeded in making HCPs more aware of the gaze they hold onto their patients, thus encouraging them to change their actions. We attributed the lack of eye contact and lack of focus on meaningful activities to a sense of vulnerability felt by HCPs. We believe that non-verbal communications skills should be more overtly taught in medical school.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".