A policy informing qualitative study to improve the process of blood product recalls and withdrawals
Bibliographic record
Abstract
BACKGROUND: Challenges associated with blood product recalls and/or withdrawals in Canada identified a need to understand the process and identify ways in which it could be improved. With the use of qualitative techniques and a modified grounded theory approach, the current process was mapped, issues were identified, and recommendations to improve the system were developed. STUDY DESIGN AND METHODS: Potential participants were identified using a sampling strategy that included key stakeholder groups. After consenting, participants were interviewed using a semistructured interview guide. Interviews were audiotaped, transcribed, and coded using a coding scheme developed from the content of the interviews. A team approach to analysis identified relevant emergent themes and led to the development of recommendations. Draft recommendations were presented at a consensus meeting, and feedback was incorporated into the final set of recommendations. RESULTS: Forty-five interviews were conducted. Major themes arising from the data were communication, timeliness of follow-up information, and challenges related to patient notification. The current recall and/or withdrawal process was described and a new model for the recall and/or withdrawal process was developed. Nineteen recommendations were formulated: 12 general and 7 hospital-specific. CONCLUSION: Large-scale recalls and/or withdrawals involving unknown or uncertain risks can be challenging both for hospitals and for the blood supplier. However, using a qualitative research approach, recommendations and a model for improving the system were developed. Key recommendations include the development of national guidelines for notification and the use of a group of resource experts to assess risk and assist with notification decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".