Implementation analysis of a case management intervention for people with complex care needs in primary care: a multiple case study across Canada
Bibliographic record
Abstract
BACKGROUND: Case management is one of the most frequently performed interventions to mitigate the negative effects of high healthcare use on patients, primary care providers and the healthcare system. Reviews have addressed factors influencing case management interventions (CMI) implementation and reported common themes related to the case manager role and activities, collaboration with other primary care providers, CMI training and relationships with the patients. However, the heterogeneity of the settings in which CMI have been implemented may impair the transferability of the findings. Moreover, the underlying factors influencing the first steps of CMI implementation need to be further assessed. This study aimed to evaluate facilitators and barriers of the first implementation steps of a CMI by primary care nurses for people with complex care needs who frequently use healthcare services. METHODS: A qualitative multiple case study was conducted including six primary care clinics across four provinces in Canada. In-depth interviews and focus groups with nurse case managers, health services managers, and other primary care providers were conducted. Field notes also formed part of the data. A mixed thematic analysis, deductive and inductive, was carried out. RESULTS: Leadership of the primary care providers and managers facilitated the first steps of the of CMI implementation, as did the experience and skills of the nurse case managers and capacity development within the teams. The time required to establish CMI was a barrier at the beginning of the CMI implementation. Most nurse case managers expressed apprehension about developing an "individualized services plan" with multiple health professionals and the patient. Clinic team meetings and a nurse case managers community of practice created opportunities to address primary care providers' concerns. Participants generally perceived the CMI as a comprehensive, adaptable, and organized approach to care, providing more resources and support for patients and better coordination in primary care. CONCLUSION: Results of this study will be useful for decision makers, care providers, patients and researchers who are considering the implementation of CMI in primary care. Providing knowledge about first steps of CMI implementation will also help inform policies and best practices.
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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".