Exploring the impact of common assessment instrumentation on communication and collaboration in inpatient and community-based mental health settings: a focus group study
Bibliographic record
Abstract
BACKGROUND: Recognition that integrated services can lead to more efficient and effective care has made the principle of integration a priority for health systems worldwide for the last decade. However, actually bringing fully integrated services to life has eluded most health care organizations. Mental health has followed the rule, rather than the exception, when it comes integrating services. The lack of effective mechanisms to evaluate the needs of persons across mental health care services has been an important barrier to communication between professionals involved in care. This study sought to understand communication among inpatient and community-based mental health staff during transfers of care, before and after implementation of compatible assessment instrumentation. METHODS: Two focus groups were held with staff from inpatient (n = 10) and community (n = 10) settings in an urban, specialized psychiatric hospital in Ontario (Canada) - prior to and one year after implementation of compatible instrumentation in the community program. Transcripts were coded and aggregated into themes. RESULTS: Very different views of current communication patterns during transfers of care emerged. Inpatient mental health staff described a predictable, well-known process, whereas community-based staff emphasized unpredictability. Staff also discussed issues related to trust and the circle of care. All agreed that compatible assessments in inpatient and community mental health settings would facilitate communication through use of a common assessment language. However, no change in communication patterns was reported one year post implementation of compatible instrumentation. CONCLUSIONS: Though all participants agreed on the potential for compatible instrumentation to improve communication during transfers of care, this cannot happen overnight. A number of issues related to trust, evidence-based practice, and organizational factors act as barriers to communication. In particular, staff noted the need for the results of comprehensive mental health assessments to be transformed into meaningful, user-friendly clinical summaries to facilitate uptake of assessment information, and consequently use of a common assessment language across mental health settings.
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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 | 0.029 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".