On the relationship between functional hearing and depression
Bibliographic record
Abstract
OBJECTIVE: To establish the effect of self-rated and measured functional hearing on depression, taking age and gender into account. Additionally, the study investigates if hearing-aid usage mitigates the effect, and if other physical health problems and social engagement confound it. DESIGN: Cross-sectional data from the UK Biobank resource, including subjective and behavioural measures of functional hearing and multifactorial measures of depressive episodes and symptoms, were accessed and analysed using multi-regression analyses. STUDY SAMPLE: Over 100 000 community-dwelling, 39-70 year-old volunteers. RESULTS: Irrespective of measurement method, poor functional hearing was significantly (p < 0.001) associated with higher levels of depressive episodes (≤ 0.16 factor scores) and depressive symptoms (≤ 0.30 factor scores) when controlling for age and gender. Associations were stronger for subjective reports, for depressive symptoms, and the younger participants. Females generally reported higher levels of depression. Hearing-aid usage did not show a mitigating effect on the associations. Other physical health problems particularly partially confounded the effects. CONCLUSION: Data support an association between functional hearing and depression that is stronger in the younger participants (40-49 years old) and for milder depression. The association was not alleviated by hearing-aid usage, but was partially confounded by other physical health problems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".