Integrated Care: A PTSD diagnostic mechanism for a refugee reception centre
Bibliographic record
Abstract
IntroductionWithin the 65 million displaced people, many are dealing with post-traumatic stress disorder (PTSD). According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), PTSD derives from witnessing of violence and crime. Among the symptoms are avoidance of trauma-related stimuli, negative thoughts and alterations in arousal and reactivity. PTSD seriously affects functionality and transition to the new society and therefore, thorough screening is highly recommended. Assessment solely through a PTSD-related questionnaire cannot be satisfactory to reveal the difficulties faced and the intervention needed. A possible way to provide a holistic approach in dealing with PTSD, is to conduct a battery of tests in combination with simple technological aids.Theory/Methods Assessment and treatment of PTSD requires the coordination of a multi-disciplinary group that is usually lacking in refugee reception centres. The suggested set of assessments does not require application by a clinician and may serve as a solid base upon which to build the treatment plan. The aim is to explore whether the use of a battery of tests along with a smart band and a Galvanic Skin Response (GSR) sensor will provide more accurate diagnostic results, allowing a thorough assessment of the mental, functional and occupational state for refugees living in reception centers and thus, multi-disciplinary intervention planning. The Davidson Trauma Scale (DTS), the Stressful Life Events Screening Questionnaire and the Canadian Occupational Performance Measure (COPM) will be applied to adult refugees residing at Kofinou Reception Center in Cyprus. Occupational therapy students supervised by a professional will apply the tests to approximately 100 participants. This will be a within-subjects design, meant to empower the diagnostic phase of intervention. In Stage A: Participants will be wearing a wrist band for one week to record the number of steps, hours of sleep (deep / light) and heart rate. In Stage B: A Questionnaire application will be conducted, while participants will be wearing a GSR sensor to determine their relative stress levels. Results will be uploaded on a cloud-based Electronic Health Record (HER) to be further processed by a multidisciplinary group.Results / DiscussionsTreatment for PTSD involves psychotherapy, medication and occupational therapy, each of which requires a thorough assessment beforehand to design a client-centered intervention strategy. Treatment may focus on dealing with negative thoughts, learning ways to cope with symptoms, minimizing depression, anxiety, or misuse of alcohol or drugs and engaging in meaningful occupations.ConclusionsCombining a battery of tests along with introducing smart wearable technologies will provide a thorough assessment and further intervention for refugees living in reception centers. This is a critical process given the vast number of refugees arriving on a daily basis in Cyprus and Greece .Suggestions for future research Application of the assessment battery by non-clinicians may be one of the next steps. Furthermore, Machine Learning / Artificial Intelligence techniques may be used to process results stored on the cloud in order to provide early stage indications of PTSD.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.002 |
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".