Technology meets clinical practice: Keel Mind as a digital therapy platform
Bibliographic record
Abstract
Purpose The purpose of this paper is to highlight current utilizations of advanced technology within the digital mental health platform Keel Mind. Design/methodology/approach Keel Mind, based out of Ontario, Canada is a therapeutic delivery platform that dually functions to train clinicians and deliver virtual therapy to clients. Findings From personalizing client and therapist matching to using natural language processing (NLP) and artificial intelligence (AI)-driven features Keel Mind supports clinician decision-making to meet the growing mental health needs of the population. Keel Mind functions to provide accessible service, efficiently train high-quality clinicians and enhance clinical outcomes for clients. In a preliminary study within post-secondary students from Ontario, Canada, 90.1% of clients reported liking the platform, and 83.4% reported that they experienced positive mental health outcomes as a result of using Keel Mind. Practical implications Keel Mind is a leader within the rapidly developing field of telepsychology and aims to promote positive mental health outcomes worldwide. Originality/value This digital showcase is the first published piece of work highlighting the technological capabilities of Keel Mind as a digital therapeutic platform. The intention of this work is to highlight current uses of digital technology within the field of therapeutic practice and to promote technological growth within the field.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".