Drivers that influence the development of care and service pathways for mental health clients in health and social service organizations
Bibliographic record
Abstract
Background: Investigation, diagnosis, treatment, and follow-ups are the stages of care to be integrated into a care trajectory for clients requiring mental health care (MSSS, 2021). As a result of recent structural reforms, health care organizations are focusing on more integrated care and services in order to respond effectively to the complex needs of this clientele, which poses challenges for the institutionalization of clinical and management practices aimed at better integrating care and services. Objective: To identify the drivers that influence the development of care and service pathways for mental health clients in health and social service organizations. Method: Cross-sectional, descriptive, qualitative, single-case study (CISSS in Quebec) with nested levels of analysis covering the settings of a ""Mental Health Care"" trajectory. The settings include the first, second, and third levels of care. Guided by Longpré's (2017) framework (LO-DISS) linking governance, organizational, structural, and clinical processes with key organizational resources, an interview guide was developed. Clinical professionals, managers, and administrators (N = 10) participated in semi-structured interviews that were analyzed using a qualitative data processing tool (NVivo). Result: Drivers of integrated care and services for mental health care pathways have been identified for each component of the LO-DISS model (2017). These highlighted processes include interdisciplinary collaboration, the patient-partner approach, quality assessment mechanisms, the development of integrative roles (case manager), and the mobilization of environmental and technological resources (electronic records, an environment that supports confidentiality and collaboration). The findings support the notion that investments in resources, processes, also clinical and management practices are key to integrating care and services and providing quality of care. Recommandations: Using the LO-DISS care and services integration model to support renewed clinical governance allows for the deployment of strategies aimed at formalizing clinical and management practices that link the various mental health care pathways experienced by the population.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 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".