Health care professionals' perspectives on screening and management of gestational diabetes mellitus in public hospitals of South India – a qualitative study
Bibliographic record
Abstract
Women developing Gestational Diabetes Mellitus (GDM) are subsequently at a higher risk of developing Type 2 Diabetes later in life. Screening and effective management of women with GDM are essential in preventing progression to type 2 diabetes mellitus. We aimed to explore the perspectives of healthcare providers regarding the barriers from the health system context that restrict the timely screening and effective management of GDM.We conducted six in-depth interviews of health care providers- four with nurses and two with obstetricians in the public hospitals in India's major city (Bengaluru). The interviews were conducted in the preferred language of the participants (Kannada for nurses, English for the obstetricians) and audio-recorded. All Kannada interviews were transcribed and translated into English for analysis. The primary data were analyzed using the grounded theory approach by NVivo 12 plus. The findings are put into perspective using the socio-ecological model.Health care providers identified delayed visits to public hospitals and stress on household-level responsibilities as barriers at the individual level for GDM screening. Also, migration of pregnant women to their natal homes during first pregnancy is a cultural barrier in addition to health system barriers such as unmet nurse training needs, long waiting hours, uneven power dynamics, lack of follow-up, resource scarcity, and lack of supportive oversight. The barriers for GDM management included non-reporting women to follow - up visits, irregular self-monitoring of drug and blood sugar, trained staff shortage, ineffective tracking, and lack of standardized protocol.There is a pressing need to develop and improve existing GDM Screening and Management services to tackle the growing burden of GDM in public hospitals of India.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".