A Communicative Intervention to Improve the Psychoemotional State of Critical Care Patients Transported by Ambulance
Bibliographic record
Abstract
BACKGROUND: Communication is key to understanding the emotional state of critical care patients. OBJECTIVE: To analyze the effectiveness of the communicative intervention known as CONECTEM, which incorporates basic communication skills and augmentative alternative communication, in improving pain, anxiety, and posttraumatic stress disorder symptoms in critical care patients transported by ambulance. METHODS: This study had a quasi-experimental design with intervention and control groups. It was carried out at 4 emergency medical centers in northern Spain. One of the centers served as the intervention unit, with the other 3 serving as control units. The nurses at the intervention center underwent training in CONECTEM. Pretest and posttest measurements were obtained using a visual analog scale to measure pain, the short-version State-Trait Anxiety Inventory to measure anxiety, and the Impact of Event Scale to measure posttraumatic stress disorder symptoms. RESULTS: In the comparative pretest-posttest analysis of the groups, significant differences were found in favor of the intervention group (Pillai multivariate, F2,110 = 57.973, P < .001). The intervention was associated with improvements in pain (mean visual analog scale score, 3.3 pretest vs 1.1 posttest; P < .001) and posttraumatic stress disorder symptoms (mean Impact of Event Scale score, 17.8 pretest vs 11.2 posttest; P < .001). Moreover, the percentage of patients whose anxiety improved was higher in the intervention group than in the control group (62% vs 4%, P < .001). CONCLUSION: The communicative intervention CONECTEM was effective in improving psychoemotional state among critical care patients during medical transport.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".