Lifetime trajectories of socio-economic adversity and their associations with psychosocial factors and attitudes towards social class
Bibliographic record
Abstract
Scientific understanding of the associations between socio-economic adversity and other domains such as health and psychosocial functioning may be improved by employing extensive, prospective life course data to model inter-individual heterogeneity in socio-economic trajectories. This study applied Latent Class Growth Analysis to derive a typology of trajectories of socio-economic adversity, and compared the psychosocial profiles of the groups based on this typology. Data were used from 2,950 men and women participating in the MRC National Survey of Health and Development in Great Britain, ascertained prospectively since birth in 1946 until age 53. Trajectories of socio-economic adversity were based on indicators of occupational class, overcrowding, housing tenure, household amenities and financial hardship at ages 4, 11, 15, 36, 43 and 53, and education at age 26. Psychosocial factors included parental interest in education, self-management, neuroticism and attitudes towards social class and social mobility. Seven distinct trajectories were identified: persistent high; persistent low; strongly declining; gradually declining; increasing; early childhood; and relapsing high adversity. Key findings include that those with increasing adversity had high parental interest in education but low self-management and high neuroticism; that those with only early childhood adversity had a less favourable psychosocial profile than those with persistent low exposure; and that groups with declining adversity had relatively favourable attitudes towards education. Findings emphasise the need to consider socio-economic and personality mechanisms in the context of one another in order to better understand later life inequality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".