Understanding facilitators and barriers to oxygen therapy for patients with interstitial lung disease
Bibliographic record
Abstract
BACKGROUND: Interstitial lung disease (ILD) is comprised of a heterogeneous group of pulmonary diseases. Oxygen therapy is used in patients with advanced lung disease; however, there are challenges associated with initiation of oxygen therapy specific to individuals with ILD. The key objectives of this study were to create a common understanding of the facilitators and barriers to oxygen therapy for patients with ILD, and healthcare professionals (HCP) caring for patients with ILD. METHODS: This qualitative study included 1 hour semistructured focus groups/interviews. An iterative and concurrent process was used for data collection and analysis to allow for supplementary development of themes and concepts generated. Data analysis used a three-phase approach: coding, categorising and development of themes. RESULTS: A total of 20 patients and/or caregivers and 31 HCP took part in 34 focus groups/interviews held over 3 months (November 2022-January 2023). Facilitators to oxygen therapy were identified including support from HCP and support groups, the perseverance and self-advocacy of patients, a straightforward administrative process and vendors/private industry that expedite access to oxygen therapy. There were also several barriers to accessing oxygen therapy for patients with ILD. The themes identified include rural disparity, testing requirements and qualifying for funding and the need for ILD-specific evidence base for oxygen therapy. CONCLUSION: Further research is needed to facilitate development of specific exertional oxygen criteria for patients with ILD, to create supports for oxygen use and monitoring and to enable providers to tailor therapy to patients. Oxygen therapy education for ILD should address the benefits and risks of oxygen therapy.
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".