Single case study of improving differentiation of self and psychological well-being of patients with Hereditary Sensory Autonomic Neuropathy-2 using Mode Deactivation Therapy
Bibliographic record
Abstract
The current research aimed to examine the effectiveness of the Mode Deactivation Therapy (MDT) on improving differentiation of self and psychological well-being of patients with HSAN-2. The research design was semi-experimental single case with multiple baselines. The statistical population of the research included 50 patients with HSAN2 all over the world from whom 2 were recruited (one from Iran and one from Canada) using purposive sampling method with inclusion-exclusion criteria. The patients filled out Skowron & Friedlander’s (1998) Differentiation of Self Inventory and Ryff’s (1989) Psychological Well-Being Questionnaire. The data were analyzed using clinical significance, visual inspect, diagnostic improvement and the six indices of efficacy. According to the results, the total percentage of improvement for differentiation of self and psychological well-being were 52.76 and 52.69, respectively. One can conclude that the Mode Deactivation Therapy (MDT) was effective in improving differentiation of self and psychological well-being of the patients with HSAN-2 through identifying maladjustment core beliefs and replacing them with useful alternatives via the VCR process.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".