Reverse relationships between utilization of personal resources and psychopathology
Bibliographic record
Abstract
Background Hobfoll’s research documented decrease in psychopathological manifestations in individuals who utilized psychosocial and material resources. We have developed a rehabilitation planning and progress monitoring instrument (Resourcefulness for Recovery Inventory –Research Edition) that fills the gap in the assessment and treatment of patients with physical and psychological conditions. This 18 bipolar scale instrument includes subscales such as: having control versus being controlled, having positive ideations versus negative, positive emotions versus negative, integration versus disintegration, and acceptance versus non-acceptance among others. The scale was standardized in Canada on more than 550 patients who suffered psycho-traumatic and physical injuries. Method 52 patients who were treated with psycho education, cognitive therapy and hypnotherapy for panic and generalized anxiety disorders at the Moscow Research Institute of Psychiatry were administered RRI-RE pre-and post along with various measures of psychopathology (such as SCL-90-R, BDI, The Spielberger State Trait Anxiety Inventory, Five-Factor Mindfulness Questionnaire, Mindful Attention Awareness Scale, and Qualitative-Quantitative Clinical Scale). Nonparametric statistics (Wilcoxon matched pairs test and Mann-Whitney Test) were used to make comparisons between T1 and T2. Conclusion The treatment group have improved to various levels (ranging from p<.0001 to p<.002) on the measures of psychopathology as compared to the control group. The subgroup that achieved full recovery was characterized by the substantial improvement on the RRI-RE negative scales, while the subgroup showing partial recovery demonstrated moderate improvements on both RRI-RE positive and negative scales but to a lesser degree than the group with full recovery.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".