Assessing COVID-19 pandemic’s impact on essential diabetes care in Manila, the Philippines: A mixed methods study
Bibliographic record
Abstract
The COVID-19 pandemic directly increased mortality and morbidity globally. In addition, it has had extensive indirect ill effects on healthcare service delivery across health systems worldwide. We aimed to describe how patient access to diabetes care was affected by the pandemic in Manila, the Philippines. We used an explanatory, sequential mixed method approach including a cross-sectional survey (n = 150) and in-depth interviews of patients (n = 19), focus group discussions of healthcare workers (n = 22), and key informant interviews of health facility administrators (n = 3) from October 2021 to January 2022. Larger proportions of patients reported absence of livelihood (67.3%), being in the lowest average monthly household income group (17.3%), and disruptions in diabetes care (54.0%) during the pandemic. They identified the imposition of lockdowns, covidization of the healthcare system, and financial instability as contributors to the reduced availability, accessibility, and affordability of diabetes-related consultations, medications, and diagnostics. At least a quarter of the patients experienced catastrophic health expenditures across all areas of diabetes care during the pandemic. Most healthcare workers and administrators identified telemedicine as a potential but incomplete tool for reaching more patients, especially those deemed lost to follow-up. In the Philippines, the pandemic negatively impacted access to essential diabetes care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".