Implementation of a Knowledge Management System in Mental Health and Addictions: Mixed Methods Case Study
Bibliographic record
Abstract
BACKGROUND: Mental health and addictions (MHA) care is complex and individualized and requires coordination across providers and areas of care. Knowledge management is an essential facilitator and common challenge in MHA services. OBJECTIVE: This paper aimed to describe the development of a knowledge management system (KMS) and the associated processes in 1 MHA program. We also aimed to examine the uptake and use, satisfaction, and feedback on implementation among a group of pilot testers. METHODS: This project was conducted as a continuous quality-improvement initiative. Integrated stakeholder engagement was used to scope the content and design the information architecture to be implemented using a commercially available knowledge management platform. A group of 30 clinical and administrative staff were trained and tested with the KMS over a period of 10 weeks. Feedback was collected via surveys and focus groups. System analytics were used to characterize engagement. The content, design, and full-scale implementation planning of the KMS were refined based on the results. RESULTS: Satisfaction with accessing the content increased from baseline to after the pilot. Most testers indicated that they would recommend the KMS to a colleague, and satisfaction with KMS functionalities was high. A median of 7 testers was active each week, and testers were active for a median of 4 days over the course of the pilot. Focus group themes included the following: the KMS was a solution to problems for staff members, functionality of the KMS was important, quality content matters, training was helpful and could be improved, and KMS access was required to be easy and barrier free. CONCLUSIONS: Knowledge management is an ongoing need in MHA services, and KMSs hold promise in addressing this need. Testers in 1 MHA program found a KMS that is easy to use and would recommend it to colleagues. Opportunities to improve implementation and increase uptake were identified. Future research is needed to understand the impact of KMSs on quality of care and organizational efficiency.
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 | 0.024 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".