Reading books in the second half of life: What correlations are there with aspects of quality of life and health?
Bibliographic record
Abstract
This DZA Aktuell is intended to provide an initial contribution on book reading and possible positive correlations with health-related aspects in the second half of life. In addition to differentiations according to age, gender and educational status, correlations between the volume of reading and emotional well-being (positive affect), subjective health and cognitive performance are presented.Key messages:The average number of books read has remained largely stable over the last 20 years. People between the ages of 46 and 85 read an average of eight to nine books a year between 2008 and 2021, compared to just seven in 2002.Around two-fifths of people in the second half of life are avid readers, reading at least 6 books a year. A good quarter of all respondents, on the other hand, do not read at all.A differentiation by education and gender clearly shows that highly educated people and women are particularly likely to be avid readers. In 2021, around half belonged to the group of avid readers, but also a good quarter of respondents with a low level of education. However, reading has nothing to do with age. All age groups read roughly the same amount.Over 85 per cent of avid readers reported positive feelings. Among non-readers, this proportion was significantly lower at 72 per cent to 79 per cent.Almost two thirds of avid readers rate their health as good or very good. Among non-readers, on the other hand, there are roughly as many people with poor as good subjective health.In the cognition test conducted in 2017, 96 per cent of avid readers scored well. This was only the case for around 89 per cent of non-readers
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".