Community-based health screening camps for early detection and diagnosis of Chronic Respiratory Diseases (CRD)s in Pune, India
Bibliographic record
Abstract
Background: CRDs are a growing health burden in LMICs, with many cases going undiagnosed due to limited access to healthcare and diagnostics in rural areas. Barriers such as rurality, financial constraints, and low awareness further delay diagnosis and treatment. We undertook accessible community-based screening camps to promote early CRD detection. Methods: We used a questionnaire to identify people with symptoms suggestive of CRD who were reviewed at local ‘camps’ with clinical examination, spirometry and expert counselling. The pulmonary rehabilitation (PR) team chest physician, spirometry technician, physiotherapist, psychologist and health educator accompanied the camps and were subsequently interviewed to explore their learnings from camps. Results: Of 314 participants screened, 132 (42.0%) were diagnosed with CRDs at eight screening camps conducted at community settings. Before attending a camp, participants had typically seen a general practitioner and been prescribed bronchodilators based on their clinical presentation. Poor access to specialist care, cost and transport challenges meant that 282 (89.8%) of screened participants had spirometry assessed for first time at the camp. Awareness of symptoms should be emphasised rather than specific diseases. Psychosocial factors such as stigma and caregiver stress significantly affect patient’s engagement with their CRD treatments. Patients and local healthcare providers have low awareness about the benefits of PR. Conclusion: Conducting community-based health screening camps improved awareness and early detection of CRDs in rural areas offering potential to tailor treatment and improve health outcomes.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".