Delphi-validation of a Psychotherapeutic Competencies Training Protocol (PCTP) for the treatment of depression in primary care: evidence-based practice and practice-based evidence ( <i>Validación Delphi de un Protocolo de Entrenamiento en Competencias Psicoterapéuticas (PECP) para el tratamiento de la depresión en atención primaria: práctica basada en la evidencia y evidencia basada en la práctica</i> )
Bibliographic record
Abstract
Following the guidelines of practice-oriented research (POR), a Psychotherapeutic Competencies Training Protocol (PCTP-1) for treating depression in primary health care (PHC) was developed and validated by a group of clinician-researchers, grounded on evidence-based practice and practice-based evidence. This protocol was subjected to a Delphi validation by a panel of judges, including clinicians, researchers and public health experts. After three rounds, a consensus of 85% in all PCTP-1 modules was achieved, resulting in version 2 of the protocol (PCTP-2), which will be used to develop a Psychotherapeutic Competencies Training Online Programme (PColP) for the treatment of depression in PHC. The basic principles of the protocol and its validation process are described and discussed, underscoring its contribution to clinicians who must face the challenges of treating patients with depression in PHC.
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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 | 0.393 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".