Survey of Former Audiologists: Reasons for Leaving the Profession
Bibliographic record
Abstract
PURPOSE: Audiologists play an essential role in hearing health care. It has been predicted that the supply of audiologists may fail to meet future market demand. One way to improve the number of available audiologists is to improve retention. The purpose of this study was an exploration of audiologist attrition as a first step toward creating strategies to improve retention. METHOD: A survey completed by 47 former audiologists included questions about demographics, why participants entered and exited the audiology profession, and job satisfaction. RESULTS: Participants cited lack of reward as the most common reason for leaving the profession. About a third disliked the for-profit hearing aid dispensing aspect of the profession, and a few would return to the profession for an audiology job that did not involve hearing aid dispensing. About a quarter left audiology to pursue other opportunities (e.g., selling a private practice), and about a quarter reported poor psychosocial work environment. CONCLUSION: Findings highlight the need for national efforts focused on (a) improving audiology awareness so students have a greater understanding of audiology as they are exploring career choices, (b) advocating for improved compensation overall and compensation models that de-emphasize sales-based financial incentives, and (c) creating strategies to help improve audiologists' work environment and opportunities for leadership roles. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.28599341.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".