Therapeutic Alliance in Online and Face-to-face Psychological Treatment: Comparative Study
Bibliographic record
Abstract
BACKGROUND: Since the COVID-19 pandemic, the number of online mental health treatments have grown exponentially. Additionally, it seems inevitable that this technical resource is here to stay at health centers. However, there is still very little scholarly literature published on this topic, and therefore, the impact of the changes that have had to be dealt with in this regard has not been studied. OBJECTIVE: This study aims to evaluate the differences in the establishment of the therapeutic alliance (TA) based on the intervention modality (online or face-to-face), the type of attachment, and diagnosis. METHODS: A total of 291 subjects participated in the study, 149 (51.2%) of whom were men and 142 were (48.8%) women between the ages of 18 and 30 years. The instruments used were sociodemographic data, SOFTA-o (System for Observing Family Therapeutic Alliances-observational), and Relationship Questionnaire. RESULTS: The results show that the treatments conducted face-to-face obtain significantly better scores in the creation of the TA than those conducted online (t=-42.045, df=289, P<.001). The same holds true with attachment, in that users with secure attachment show a better TA than those with insecure attachment (t=6.068, P<.001,), although there were no significant differences with the diagnosis (F=4.566, P=.44), age (r=0.02, P=.70), and sex (t=0.217, P=.33). CONCLUSIONS: We believe that professionals are not yet prepared to conduct remote treatment with a degree of efficacy similar to that of face-to-face. It is essential for professionals to receive training in this new technical resource and to understand and incorporate the variants it entails into their daily practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".