Psychological Work Environment and Professional Satisfaction Among Indian Audiologists
Bibliographic record
Abstract
Objective : The study examined self-reported work environment and professional satisfaction among Indian audiologists. Method : A cross-sectional online survey was conducted using the demand-control-support questionnaire (DCSQ), a short version of the effort-reward-imbalance (ERI) questionnaire and open-ended questions to explore professional issues. Seventy-one Indian audiologists participated in the survey. Results : No association was found between demographic factors (i.e., gender, education, work type, and work settings) and the DCSQ and ERI sub-scales. Using the demand control model, 14% of audiologists reported working in a high-stress psychological work environment. Using the ERI ratio to estimate the imbalance between efforts and reward, it was observed that 72% of the participants experienced unfavourable working situations where the reward did not correspond to the effort made. Audiologists identified various professional issues including ‘lack of awareness of the profession among public’ and ‘unethical practice by other professionals and unqualified people is a concern’, and also made some suggestions on how to overcome them. Conclusions : The results suggest that a high percentage of audiologists perceive to be practising in high effort-low reward working conditions in comparison with audiologists in other countries such as Sweden. Further work is required to understand, and possibly overcome various professional concerns raised by audiologists.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".