Lessons from professional nursing associations' policy advocacy responses to the <scp>COVID</scp> ‐19 pandemic: An interpretive description
Bibliographic record
Abstract
BACKGROUND: Professional nursing associations across jurisdictions engaged in significant policy advocacy during the COVID-19 pandemic to support nurses, the public and health systems. While professional nursing associations have a long history of engaging in policy advocacy, scholars have rarely critically examined this important function. PURPOSE: The purpose of this study was twofold: (a) to examine how professional nursing associations engage in the process of policy advocacy and (b) to develop knowledge specific to policy advocacy in the context of a global pandemic. METHODS: This study was conducted using interpretive description. A total of eight individuals from four professional nursing associations (two local, one national and one international) participated. Data sources included semi-structured interviews conducted between October 2021 and December 2021 and internal and external documents produced by organizations. Data collection and analysis occurred concurrently. Within-case analysis was conducted prior to cross-case comparisons. FINDINGS: Six key themes were developed to illustrate the lessons learned from these organizations including their organization's role in supporting a wide audience (professional nursing associations as a compass); the scope of their policy priorities (bridging the gaps between issues and solutions), the breadth of their advocacy strategies (top down, bottom up and everything in between), the factors influencing their decision-making (looking in and looking out), their evaluation practices (focus on contribution, not attribution) and the importance of capitalizing on windows of opportunity. CONCLUSIONS: This study provides insight into the nature of policy advocacy carried out by professional nursing associations. IMPACT: The findings suggest the need for those leading this important function to think critically about their role in supporting a wide range of audiences, the breadth and depth of their policy priorities and advocacy strategies, the factors that influence their decision-making, and the ways in which their policy advocacy work can be evaluated to move towards greater influence and impact.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".