Perceived Causal Relations
Bibliographic record
Abstract
Researchers have argued that the investigation of causal interrelationships between symptoms may help explain the high comorbidity rate between certain psychiatric disorders. Clients' own attributions concerning the causal interrelationships linking the co-occurrence of their symptoms represent data that may inform their clinical case conceptualization, treatment, and psychological theory regarding the etiology of comorbid disorders. The present study developed and evaluated a novel psychological assessment methodology for measuring Perceived Causal Relations (PCR) and examined its psychometric properties as applied to the question of whether posttraumatic stress and anxiety symptoms represent causal risk factors for depressive symptoms in 225 undergraduates. Participants attributed their symptoms of anxiety and posttraumatic reexperiencing as significant causes of their depressive symptoms. Exploratory analyses identified a listing of symptoms reliably attributed as significant causes of other symptoms and functional impairment, as well as a listing of symptoms reliably attributed as significant effects (outcomes) of other symptoms and functional impairment. The PCR method has promise as an idiographic approach to assessing the causes and consequences of comorbid psychiatric symptoms and associated functional impairment. Research is required to assess the relevance and replicate these findings in distinct psychiatric groups experiencing various symptomatic presentations. Future research may also examine PCR ratings associating other individual differences, for example, between measures of history (e.g., life events), life choices, and personality.
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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 | 0.022 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".