Key performance indicators for mental health and substance use disorders: a literature review and discussion paper
Bibliographic record
Abstract
With an increasing recognition of the importance of mental and substance use disorders (MESUDS) for population health and health systems and the potential value of systems-based performance indicators in addressing this issue, we aimed to describe the development and content of key performance indicators (KPIs) for MESUDS. Publications were identified through official websites, Google searches and PubMed. Following “PRISMA” guidelines, 25 studies were kept for qualitative synthesis and six for quantitative analysis. We describe their use in practice by comparing their application across a range of public and mixed healthcare systems. Currently, the KPI development for MESUDS adopts several methodologies, including expert opinion, literature review, stakeholder consultation, and the structured consensus method. The rationales provided for selection of particular KPIs vary greatly between systems. Systems exhibit different levels of KPI adaptability, which is reflective of dynamic changes in evidence-based practices. We noted bias in the level of KPI assessment toward system/health plan evaluation followed by program/service evaluation. Similarly, there is a large skew toward KPIs that reflect evaluation of processes. Collection of data in all systems is nearly exclusively reliant on electronic administrative/medical data. Experiences from these systems are synthesized into methodological recommendations and considerations for further research and clinical practice are provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".