Perceptions of Health Care Providers on the Use of the Edmonton Dyspnea Inventory for Dyspnea Assessment in Interstitial Lung Disease Patients
Bibliographic record
Abstract
Abstract RATIONALE: Dyspnea is a prevalent and distressing symptom in interstitial lung diseases (ILD) with significant effects on patients’ quality of life and is also associated with poor prognosis. Many guidelines recommend a multidimensional dyspnea assessment tool to assess dyspnea and guide management; however, this is not routinely used in ILD care. We developed a novel 9-item scale, the Edmonton Dyspnea Inventory (EDI), in which dyspnea severity is rated across different settings including at rest, during activities of daily living, and self-reported exercise and crises. This standardized, multidimensional tool captures dyspnea intensity for specific contexts, which clinicians can use to manage dyspnea more effectively. Our early studies support the feasibility of the use of EDI in outpatient settings. Healthcare professionals (HCP) are major stakeholders in the implementation of this tool; therefore, this study explored the perceptions of community health care professionals regarding the use of EDI. METHODS: We conducted a qualitative study using an inductive approach and open coding for content analysis. Email invitations for study participation were sent to community HCPs. Informed consent was obtained from study HCP participants. Two focus groups and one key informant interview were conducted. Themes were extracted from transcript and field notes analyses. RESULTS: Four main themes were identified: 1) EDI is a meaningful clinical tool, 2) HCP explicitly engage and educate patients for effective use of EDI, 3) HCP use EDI to personalize and evaluate dyspnea management, and 4) EDI is valuable for communication and interprofessional collaboration. Theme one: HCP reported that EDI offers a comprehensive framework for dyspnea assessment, HCP conveyed the commitment required for symptom management, and EDI facilitated HCP and patients to plan and manage dyspnea. Theme two: HCP discussed the need to educate patients around the purpose of EDI, tailor EDI to specific patient characteristics (ex. language barrier, cognitive barriers), and engage caregivers. Theme three: HCP used the EDI to tailor dyspnea management, allow for monitoring of symptoms, and early detection of worsening symptoms for timely introduction of interventions. Theme four: HCP shared that EDI enables a common understanding of dyspnea among colleagues and patients and encouraged dissemination of EDI to allow for the “same language” to be spoken. CONCLUSION: The EDI was found to be a valuable tool to assess dyspnea and personalize dyspnea management by HCPs. They recommended EDI to be used in clinical practice and education for dyspnea management among ILD patients.
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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 | 0.015 | 0.044 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".