Practical Applications of Case Management Software for Practitioners in Health and Human Services
Bibliographic record
Abstract
This chapter aims to demonstrate the capabilities and practical applications of a case management software system for not-for-profit organizations. Whilst a variety of software systems are available, for the purposes of this chapter the authors will use/refer to “Penelope,” a system designed by Athena, a Kitchener, Ontario software company. Penelope is a sophisticated piece of technology that can collect and analyze information on clients, services, human resources and outcomes. Although a wide range of data can be collected using this software, it cannot make decisions about what to record or how to scrutinize the data. The “what to record” and the “how to analyze and interpret” are management and clinical decisions. Effective use of case management software requires technical, management and analytical skills combined with leadership and imagination. Case management software systems and the information they provide are only as good as the planning and organizational systems in which they are used. The best starting point for practitioners and managers seeking answers to questions about their clients, services, community and policy, and to maximize the capabilities of case management software systems is the development and application of a program logic model unique to the agency in question. A program logic model focuses on the services and programs provided by the agency describing its’ inputs, activities, outputs and outcomes. Understanding how to develop and apply their model allows practitioners and managers to perform tasks such as performance monitoring, experimental and quasi experimental approaches program evaluations, and client satisfaction and outcome studies to demonstrate the effectiveness of their services to funding bodies, consumer groups, and their boards of directors whilst also providing a tool that can be used to enhance agency performance.
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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 | 0.041 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.010 |
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".