Contextual Factors Influencing the Effects of a Quality Improvement Support Agency (QISA): A Qualitative Study
Bibliographic record
Abstract
Quality improvement has become a global priority as more countries become interested in systemic strategies, particularly that of the Quality Improvement Support Agency (QISA). This study sets out to identify and document contextual factors that facilitate or hinder the perceived effects of QISAs. A study of two critical and paradigmatic cases consisting of the QISAs HAS and INESSS was carried out through interviews, a focus group discussion, a non-participatory observation session, and the use of secondary data from in-depth documentary research. All data were recorded and processed using the QDA Miner 6.0.2 software in an inductive approach in two coding cycles and were validated by the participants. The results showed that the contextual factors at the level of the internal environment included: leadership of agency actors; dissemination and support strategy for their productions and support for their implementation; availability and nature of human, informational, and material resources; organizational culture; training of actors; and coordination, coherence, and complementarity of the various activities of an agency. At the level of the external environment, the factors included: crises; institutional collaborations and partnerships at the national and international levels; collaborations and partnerships with system actors; public policy, governance, and leadership; legal provisions; health information system and data accessibility; and funding. These results, the first to be produced on this topic, complement the literature on QISAs and may thus inspire other jurisdictions in developed and developing countries to implement more efficient, sustainable, and self-potentiating quality improvement strategies.
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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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".