Patient and provider perceptions of the impact of COVID-19 on tuberculosis healthcare access and delivery: an interpretive description study of the complexities of a pandemic within a pandemic in Alberta, Canada
Bibliographic record
Abstract
Introduction We aimed to explore patient and provider perspectives of the impact of the COVID-19 pandemic on tuberculosis healthcare access and service delivery. Methods We used interpretive description, a qualitative approach with the end-goal of informing decisions and actions in clinical practice. Levesque et al ’s ‘Conceptual framework of access to health care’ informed the development of our interview guides. Interviews were conducted virtually and confidentially transcribed verbatim. Data generation and analysis occurred concurrently. Analysis was informed by Braun and Clarke’s six phases of reflexive thematic analysis. Strategies to enhance rigour and trustworthiness of the findings were used. Results We completed 15 interviews: 6 with patients and 9 with providers. Three key themes were generated: (1) diagnostic hurdles created delay; (2) hybrid services promote health equity; and (3) navigating the complexities of a pandemic within a pandemic. Diagnosing tuberculosis was challenging even prior to the pandemic since some providers lacked experience and familiarity with the condition. The diagnostic process was further complicated with the onset of the COVID-19 pandemic. However, COVID-19 also introduced streamlined virtual care for patients which was convenient and improved access but was not viewed as being equivalent to in-person care. The intersection of the COVID-19 and tuberculosis pandemics created competition for limited resources while highlighting learnings that may positively impact future tuberculosis care. Conclusions Our findings can inform health system leadership about how the COVID-19 pandemic impacted care of other public health threats like tuberculosis, helping to prepare more effectively and equitably for future challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".