The mapping of emotions in a respiratory illness: Transferability of illness experience from Pulmonary Arterial Hypertension to COVID-19
Bibliographic record
Abstract
Objectives: Covid-19 poses an existential threat that has increased death anxiety at the individual and societal levels. In prior work, we have examined existential conversations in patients with Pulmonary Arterial Hypertension (PAH), an incurable respiratory disease with symptom overlap. In this mixed method study, we analyse the emotional qualities of these conversations in PAH. By understanding the emotions in PAH, we may learn something about the feelings that can also be evoked in people coping with Covid-19. Methods: We interviewed 30 PAH patients from 2016-2018 about the meaning and impact of illness on their lives. We analysed transcripts and audio recordings for heightened emotional moments and categorised the emotional responses and topics that were discussed. A multiple correspondence analysis was conducted to identify the associations between emotions and topics. Clini cal illustrations are provided for interpretation. Results: Mean age and illness duration was 52 and 6 years, and 77% were female. Participants had a mean of 5 emotional moments, each lasting on average 20 seconds. Half occurred in the first 20 minutes. Coping with diagnosis and the healthcare system was accompanied by feelings of shock and unfairness; relational issues involving close others evoked complicated feelings of isolation, worthlessness, and self-blame; and the experience of physical limitations and mortality salience elicited much anger and fear. Conclusion: People confronted by the threat of mortality from disease may have powerful feelings that they would benefit from sharing. These emotions are readily expressed because opportunities to discuss them are rare. Psychoeducation about illness experiences may help healthy people to relate to the medically ill and destigmatise the discussion of illness-related concerns. Research on coping with existential distress may be applied to the illness experience of Covid-19.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".