Explain Breathlessness: Could ‘Usual’ Explanations Contribute to Maladaptive Beliefs of People Living with Breathlessness?
Bibliographic record
Abstract
BACKGROUND: Explanations provided by healthcare professionals contribute to patient beliefs. Little is known about how healthcare professionals explain chronic breathlessness to people living with this adverse sensation. METHODS: A purpose-designed survey disseminated via newsletters of Australian professional associations (physiotherapy, respiratory medicine, palliative care). Respondents provided free-text responses for their usual explanation and concepts important to include, avoid, or perceived as difficult to understand by recipients. Content analysis coded free text into mutually exclusive categories with the proportion of respondents in each category reported. RESULTS: = 61) were predominantly clinicians (93%) who frequently (80% daily/weekly) conversed with patients about breathlessness. Frequent phrases included within usual explanations reflected breathlessness resulting from medical conditions (70% of respondents) and physiological mechanisms (44%) with foci ranging from multifactorial to single-mechanism origins. Management principles were important to include and phrases encouraging maladaptive beliefs were important to avoid. The most frequent difficult concept identified concerned inconsistent relationships between oxygenation and breathlessness. Where explanations included the term 'oxygen', a form of cognitive shortcut (heuristic) may contribute to erroneous beliefs. CONCLUSIONS: This study presents examples of health professional explanations for chronic breathlessness as a starting point for considering whether and how explanations could contribute to adaptive or maladaptive breathlessness beliefs of recipients.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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 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".