The Blood Pressure Lowering Qualities of a Trauma-Focussed Cognitive Behavioural Therapy Approach
Bibliographic record
Abstract
Background: High blood pressure is a serious condition which can make suffers vulnerable to developing conditions such as stroke. Even though research identifies that there can be a link between developing high blood pressure and psychological factors there is little research available to establish how psychological treatment can help sufferers. Instead most of the studies focus on physical interventions and lifestyle changes.Objective: This article aims to share a novel and interesting case example which indicated a potential link between trauma-focussed cognitive behavioural therapy, symptoms of PTSD, and a reduction in high blood pressure.Method: The client was provided with trauma focussed CBT specifically used to target her trauma of an accident which she suffered. The techniques in therapy utilised were, psychoeducation of the therapy model, relaxation therapy, re-living of the trauma, imagery rescripting and systematic desensitization. Following therapy the client recovered from all of her post trauma symptoms and even though her blood pressure was not specifically targeted in treatment, she experienced a corresponding reduction in her blood pressure following treatment.Conclusion: Discussion is centred around how this provides opportunities for researchers to investigate whether there is a causal link between the use of psychological approaches in the reduction in blood pressure level. Further research , for example correlational studies is recommended in this field.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".