Methodological Considerations on Psychological Diagnosis and Consulting in Clinical Settings
Bibliographic record
Abstract
Background: The interest of diagnosis in clinical psychology is growing, but psychological diagnosis is a complex construct to describe because it is not a static process. It can be subject to change due to continuous evolutions/involutions present in a person’s psychic life. This review aims to describe the concept of psychological-clinical diagnosis. Method: We have carried out, following PRISMA guidelines, a systematic review of literature on Pubmed. Data for this systematic review have been collected in compliance with the reporting elements used for systematic reviews and metanalysis. Search terms were "Psychological diagnosis” AND “Psychological formulation”. Results: Psychological diagnosis can be defined as a process through which clinicians aim to know people's psychic functioning to investigate conscious and unconscious, healthy or pathological psychic dimensions. Psychological diagnosis has great importance in the clinical setting. Conclusion: Psychological diagnosis can be carried out at different levels, according to the context in which it is applied, using a multi-instrumental and multidimensional to investigate the state of an individual's functioning. The literature about this topic is poor and often connected to psychiatric diagnosis, which reduces its particular features. In light of this, it is important to give an overview of the concept of psychological-clinical diagnosis.
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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 | 0.466 | 0.713 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".