The role and value of professional membership for emergency nurses: What are the key elements?
Bibliographic record
Abstract
Granovetter (1973) wrote on the ‘Strength of Weak Ties’, even though published all those years ago, there was value and recognition placed on the strength of smaller group interactions and how they can impact on more macro level patterns, the same sociological theory holds true today. There are strengths in professional memberships, whether they are conducted on a small or large scale. They are powerful vessels for nurses to have impactful interactions. We recognize that professional memberships for nursing are available globally, whether that is through a local, national, or international organization. They offer a broad range of opportunities including education, bursaries, conferences, networking, and leadership opportunities. They are seen to connect and engage with peers and in some countries professional organizations also drive change, politically lobby, or even offer professional indemnity insurance or union support. Despite a strong presence in nursing professional organizations, the evidence shows that membership is declining or staying stagnant, and it is growing more difficult to bring nurses together through these channels. So, the question must be asked, ‘Why do nurses not strongly engage with professional organizations?’ This study focuses on emergency nurses to gain a greater understanding of drivers for professional membership and engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".