The Integrated Performance Management System: A Key to Service Trajectory Integration
Bibliographic record
Abstract
INTRODUCTION: This article presents an experience of deploying an integrated performance management system as a catalyst for the integration of a service trajectory for children in vulnerable situations. Called ''Jimmy'', the project identifies how the integrated performance management system makes it possible to improve accessibility, continuity of services and well-being at work among stakeholders. METHODS: An action research was conducted in a large healthcare organization in Canada, between August 2016 and October 2018. Data was systematically collected throughout the various cycles of research using field notes, more than 350 hours of observations, 15 interviews and 3 focus groups. RESULTS: This research supports using an integrated performance management system as a model for collaborative management that supports both horizontal and vertical integration in the service trajectory. The use of visual boards and status sheet meetings were determining factors for service integration and the functioning of integrated teams. This also led to improvements in accessibility and continuity of services, as well as in employee well-being. DISCUSSION AND CONCLUSION: Supported by the various tools of the integrated performance management system, Project ''Jimmy'' reinforces the implementation of linkage and coordination models, which in turn helps create strong connections among teams. The status sheet meetings and visual boards are tools that vertically integrate different hierarchical levels and horizontally integrate various front-line stakeholders through the user-oriented trajectory.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".