Established and Nascent Entrepreneurs: Comparing the Mental Health, Self-Care Behaviours and Wellbeing in Singapore
Bibliographic record
Abstract
Mental health problems currently affect a quarter of the world's population. Recent research in western societies has started to examine the relationship between entrepreneurship and mental health problems such as Attention Deficit Hyperactivity Disorder (ADHD) and dyslexia. However, little has been done to categorize entrepreneurs into different types and investigate how their levels of mental health and well-being correspond to these types. This study divided entrepreneurs into established and nascent categories and examined this topic in Singapore. By distributing two sets of surveys, a total of 834 responses were collected, with 346 responses from established entrepreneurs and 488 responses from nascent ones. The results showed that the nascent entrepreneurs' levels of well-being were found to be much lower than those of the established entrepreneurs. Furthermore, entrepreneurs with ADHD or dyslexia symptoms generally had a much lower level of life satisfaction, compared with those without. However, the self-care behaviours observed in this sample differed somewhat from observations made in western societies, which might be explained by the different cultures and habits in Singaporean society. The findings not only highlight the need for relevant organizations to support nascent entrepreneurs but serve to remind veteran entrepreneurs to practice more healthy self-care behaviours.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".