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 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 | 0.071 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| 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".