Which Counts More: Differential Impact of the Environment or Differential Susceptibility of the Individual?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it