Which Counts More: Differential Impact of the Environment or Differential Susceptibility of the Individual?
Bibliographic record
Abstract
The theory of differential susceptibility is helping to explain how genetic, neurological and personality factors affect individual mental and physical health and why interventions work better with certain populations. As social workers, however, our focus is more on the impact of the social determinants of health found in people’s environments and the nuanced way external factors influence psychological treatment outcomes and human development over time rather than genotypes and phenotypes. This article discusses differential impact theory (DIT) as a complementary theory to differential susceptibility in an effort to make both theories relevant to social work practice. After a brief summary of the differential susceptibility research, I draw from studies of psycho-social interventions and Person × Environment interactions to show that responsibility for positive adaptation resides within the systems that surround individuals just as much as, and possibly more than, within individuals themselves. DIT provides a more balanced explanation than differential susceptibility theory alone for why clinical and community interventions and changes to social policy can have a positive influence on psycho-social outcomes. The implications of DIT are discussed with regard to the design and delivery of psychological and social interventions.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".