What work is required to implement and sustain the National Surgical Quality Improvement Program (NSQIP)? A qualitative study of NSQIP implementation in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVES: Hospitals introducing the National Surgical Quality Improvement Program (NSQIP) face implementation challenges. To understand the work of embedding NSQIP into routine practice, we explored interactions between contextual factors and the work among implementation teams at the individual, team and organisational level to illuminate how to support and sustain NSQIP implementation. DESIGN: Qualitative interpretative study using thematic analysis. SETTING: Five contextually diverse hospital sites in Alberta, Canada, for in-depth interviewing and four additional hospitals for observation of NSQIP meetings. PARTICIPANTS: 9 Surgeon and Anaesthesiologist Champions; 6 Surgical Clinical Reviewers; 4 Directors and 1 Surgical Site Manager; 3 Operating Room Managers; 3 Quality Improvement Consultants; 1 Surgeon and 1 Provincial NSQIP Lead. METHODS: To capture context, process and the dynamic interplay between the two, we integrated the Consolidated Framework for Implementation Research (CFIR) and Normalisation Process Theory (NPT) to guide data collection and analysis. 28 individual semi-structured interviews with key informants and observations with field notes of 10 NSQIP meetings were conducted. Data were coded deductively and inductively and analysed thematically. RESULTS: Key findings informed by CFIR describe the impact of Provincial Collaboratives, leadership support and resources to support NSQIP work. Key findings illuminated by NPT highlight how teams overcame mistrust in NSQIP through relationship building, creating formative spaces to inform collective understandings of NSQIP and inviting feedback from professional groups to cocreate quality improvement solutions. This approach led to increased engagement with NSQIP data and encouraged shifts in conversations within and between nursing and physician groups from problems to solutions based. CONCLUSIONS: The work the teams did to implement and sustain NSQIP highlights the need for time and resources to develop shared understandings of work processes, reorganise themselves to work together and understand how to help others in the surgical community interpret and value using NSQIP to improve 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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.015 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".