Audiology India (Non-Governmental Organization): Background, Mission, and Accomplishments
Bibliographic record
Abstract
Hearing loss is a global health concern, particularly in the low- and middle-income countries. Some of the reasons for this include, higher prevalence of hearing loss in these countries, lack of adequate awareness on hearing loss and its consequences, limited access to hearing care services to in suburban/rural areas, and high cost of such services. To make matters worse, health care services provided by the government in developing countries such as India are limited, and services provided by for-profit institutions are expensive. Therefore, there is a need for other stakeholders (e.g., non-governmental organizations) to bridge this service gap. In this paper, we introduce Audiology India (AI) to readers, an organization that is striving to improve ear and hearing health care services in India. We begin this paper by providing an overview of the current status of hearing care services in India. Next, we describe the background of AI, its mission, and accomplishments. Briefly, the goals of AI are: (a) to provide community-based hearing care services to individuals with no access to mainstream ear-care; (b) to conduct campaigns to raise public awareness about hearing loss and avenues for its prevention; (c) to carry out need-based research to continuously fine-tune our services and advance audiology in India; and (d) to offer consultancy services related to ear and hearing care.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".