Mental Health Counseling & Therapy via Artificial Intelligence-Enabled Approaches
Bibliographic record
Abstract
1. IntroductionHumanity has taken great leaps to achieve the position that it is in today. Artificial Intelligence (AI) involves the development of machines or equivalent algorithms for performing tasks to reduce redundancy and increase reliability. As is with any other discovery or innovation, AI has evolved to suit the needs of the world it lives in, and its great flexibility gives it the power to be applied across fields, with a constant look for patterns or ideologies that could be used to address any problems.Intelligence has evolved beyond human intelligence, a concept widely and deeply in psychology by pioneers such as Binet, Spearman, Gardner, and many others. Machines and software are now frequently considered more “intelligent” - smarter, faster, more precise. Thus, it is not surprising that psychology and artificial intelligence have contributed to each other. Psychologists have been increasingly involved in the development of AI systems. They have been trying to ensure human biases are not implemented in the systems, among other concerns such as trust, Privacy, and how people will relate to the bots [1].The first AI system developed to act as a therapist potentially was ELIZA, principled after Carl Rogers and his client-centered approach [2]. ELIZA was a text-based chatbot that worked with natural language processing. Advancements have led to more sophisticated software, which may also learn and enhance through human communication, unlike their simpler predecessor. As discussed throughout the paper, the insurance of good mental health has been realized as an important part of any individual, and the aspects related to the same have been deliberated upon in detail. AI’s power allows it to be specifically used to address issues or problems related to mental health counseling and therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".