Experiences of Patients With Chronic Obstructive Pulmonary Disease receiving Integrated Telehealth Nursing Services During COVID-19 Lockdown
Bibliographic record
Abstract
Abstract Objective: Even though telemonitoring existed prior to COVID-19, the need was accelerated for patients with COPD due to the limited medical consultations and the anxiety for being infected. To our knowledge, no qualitative study has explored the experiences of COPD patients and the potential benefits of receiving integrated telehealth nursing services during the lockdown. Methods: Ten participants were interviewed after receiving telehealth nursing services for 3 months; data was analyzed thematically. Results: Irrespective of COPD severity, all participants expressed that the existing fragmented follow-up care was intensified during COVID. The telenursing services provided them with the comfort and reassurance that a clinician is available for support, advice, and care during the times of isolation. Despite diverse opinions regarding the respiratory-related questions for follow-up, all were enthusiastic about the use of the oximeter in their daily lives. None perceived sharing data as an issue, instead they encouraged the exchange across levels of care. Conclusion: Despite an appreciation for the service, it is imperative for sustainability reasons that standardized protocols are developed to balance patient preferences in terms of response frequency and the clinical data needed for a telenurse to provide appropriate interventions.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".