The Role of an Intraorganizational Digital Community in Shaping Nurses’ Professional Identities and Practice: Qualitative Interview Study
Bibliographic record
Abstract
Background: In 2017, Israel's health organizations established intraorganizational social media communities, believing that they would serve as a tool that would enable people to share experiences across regional boundaries. However, conducting preliminary studies and analyzing the findings to determine how they affected employees' experience was never part of this effort. Objective: This study examined the impact of an intraorganizational digital community on nurses' professional identities and practices within a large health care organization. Methods: Using a qualitative descriptive approach, semistructured interviews were conducted with 20 nurses from various specialties and regions participating in an intraorganizational nurses' community on Facebook. Results: The findings showed that the intraorganizational community fostered a strong sense of belonging, emotional support, and professional development among its members. Participants talked about having a sense of community, much like being a member of a family, where they could confide in one another, ask for help and advice, and receive support. Enriched professional knowledge, self-efficacy, and pride in the nursing profession were all associated with active involvement in the community. The complex interactions of social media use in a hierarchical health care system were emphatically acknowledged by addressing challenges, including information overflow and concerns about sustaining a professional persona in a public digital domain. Conclusions: Overall, the study illustrated how crucial it is for health care organizations to actively manage potential negative consequences while using the benefits of intraorganizational digital networks, such as improving supportive relationships and ongoing shared learning. This study contributes to the growing body of knowledge regarding the crossroad between social media and health care, offering insights into developing strategies to promote a supportive and connected nursing workforce. The implications are particularly relevant for organizations seeking to strengthen nurse well-being and professional development through innovative digital tools. Future research should include quantitative studies to assess an intraorganizational platform's influence on outcomes such as nurses' sense of community, professional identity, self-efficacy retention, and job satisfaction.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| 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".