Abstract 101: Qualitative Evaluation of a State Stroke Registry
Bibliographic record
Abstract
Objectives: Set within the California Stroke Registry/California Coverdell Program (CSR/CCP) - a stroke-care-related quality improvement (QI) program, this study sought to do as follows: (1) describe the program’s previous prevention efforts (through quarter 1 [Q1], 2015); (2) explore what factors were influential in bringing about this programmatic state; and (3) investigate what changes could occur, in order to usher in a better future state for the program. Methodology: Starting in quarter 2, 2015, a systematic review of twenty-seven CSR/CCP documents preceded the conduct of fourteen key informant interviews. Subsequently, content and thematic analyses of the CSR/CCP documents and interview transcripts were performed in NVivo, and, using an action research approach, key stakeholders vetted the findings and translated them into recommendations for change. Findings: (1) In terms of the CSR/CCP’s programmatic state through Q1, 2015, it was revealed that, with respect to (a) recruitment (of registry members), (b) program infrastructure, (c) partnerships, and (d) QI activities, there was misalignment not only with the program’s original guiding vision but also with the prevailing national healthcare trends at that time. Of concern was the program’s lack of a functional data collection system to monitor stroke care - a significant and far-reaching liability. (2) With regard to factors that gave rise to this sub-par programmatic state, this study suggested that certain tangible influences (e.g., historical decisions, staffing patterns, operational constraints) and intangible factors (e.g., held beliefs, a lack of visibility, a lack of programmatic fit within the larger organization) had been important. (3) With respect to changes that could enable the CSR/CCP to achieve a more optimal future state, two key recommendations emerged: (a) that the CSR/CCP ought to adopt more explicit knowledge management practices - i.e., capturing, sharing, and using informational assets; and (b) that the CSR/CCP ought to be working cross-functionally - i.e., establishing multi-disciplinary teams, intentionally-focused on specific aspects of the program’s work. Implications: Out of this project came key findings related to knowledge management and cross-functional teams. Taking action in these areas could enable the use of timely, relevant data in driving the change-related efforts of dedicated human and other resources. Such change could lead to an improved programmatic state, one that (1) is more in line with the CSR/CCP’s original guiding vision; and (2) could serve as a model of clinical medicine and public health coming together to improve health at the community level. While becoming consistent with national healthcare trends, an improved programmatic state could also have immediate local benefits, as the CSR/CCP likely would be more effective in its work to improve the quality of stroke care.
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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 | 0.107 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".