Why We Belong - Exploring Membership of Healthcare Professionals in an Intensive Care Virtual Community Via Online Focus Groups: Rationale and Protocol
Bibliographic record
Abstract
BACKGROUND: Many current challenges of evidence-based practice are related to ineffective social networks among health care professionals. Opportunities exist for multidisciplinary virtual communities to transcend professional and organizational boundaries and facilitate important knowledge transfer. Although health care professionals have been using the Internet to form virtual communities for many years, little is known regarding "why" they join, as most research has focused on the perspective of "posters," who form a minority of members. OBJECTIVE: Our aim was to develop a comprehensive understanding of why health care professionals belong to a virtual community (VC). METHODS: A qualitative approach will be used to explore why health care professionals belong to an intensive care practice-based VC, established since 2003. Three asynchronous online focus groups will be convened using a closed secure discussion forum. Participants will be recruited directly by sending emails to the VC and a Google form used to collect consent and participant demographics. Participants will be stratified by their online posting behaviors between September 1, 2012, and August 31, 2014: (1) more than 5 posts, (2) 1-5 posts, or (3) no posts. A question guide will be used to guide participant discussion. A moderation approach based on the principles of focus group method and e-moderation has been developed. The main source of data will be discussion threads, supported by a research diary and field notes. Data analysis will be undertaken using a thematic approach and framed by the Diffusion of Innovation theory. NVivo software will be used to support analyses. RESULTS: At the time of writing, 29 participants agreed to participate (Focus Group 1: n=4; Focus Group 2: n=16; Focus Group 3: n=9) and data collection was complete. CONCLUSIONS: This study will contribute to a growing body of research on the use of social media in professional health care settings. Specifically, we hope results will demonstrate an enhancement of health care professionals' social networks and how VCs may improve knowledge distribution and patient care outcomes. Additionally, the study will contribute to research methods development in this area by detailing approaches to understand the effectiveness of online focus groups as a data collection method for qualitative research methods.
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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 | 0.135 | 0.121 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".