PP101 A Procedural Method For Networking Local And Regional Stakeholders
Bibliographic record
Abstract
INTRODUCTION: In 2015, the province of Québec, Canada went through a major restructuration in its healthcare system which resulted in regional institutional merging. Our hospital-based Health Technology Assessment (HTA) unit is now part of a large network comprising fourteen institutions covering an area of 12,734 km2. This new organizational context poses major challenges in terms of addressing various local needs and for involving stakeholders into our assessments. In this paper we present how we addressed these difficulties. METHODS: This case study presents the procedural method we developed for involving local and regional stakeholders into an HTA concerning the need to extend a regional prenatal ultrasound screening program. We describe how we collected local data and networked local to regional stakeholders for producing the assessment and recommendation. RESULTS: After completion of the literature review on first trimester ultrasound screening, local data from each institution were collected using a combination of focus group meetings with local managers, gynecologists and ultrasound technologists. Overall, fifteen people were consulted on diverse regional sites, including two services users. In order to assess the perception of the results regarding efficacy, users preferences, cost and organizational impact of expanding the screening program, people were asked to complete an online survey. The results of this survey were then used to write a first draft of a recommendation. A second survey was generated in order to obtain agreement of the fifteen people regarding the recommendation. Overall, this method decreased the time required to complete the assessment and reduced project operating costs. However, divergence of opinions may be difficult to resolve by this method and many rounds of consultation may be needed. CONCLUSIONS: Our procedural method using a combination of focus groups and online surveys for collecting local and regional data and opinions from stakeholders and support recommendation, has succeeded to provide well contextualized information for supporting a decision.
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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 | 0.040 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".