Attitudes Toward Health Care Virtual Communities of Practice: Survey Among Health Care Workers (Preprint)
Bibliographic record
Abstract
<sec> <title>BACKGROUND</title> Virtual communities of practice (VCoPs) have been shown to be an effective means for knowledge and research uptake, but little is known about why health care workers choose to use them. The elaboration likelihood model (ELM) is a theoretical model of persuasion that distinguishes between different routes of information processing that influence attitude formation and change. To date, no research has investigated the antecedents to these processing routes for VCoPs within a health care setting. In understanding these determinants, VCoPs can be appropriately designed to increase their chances of use and value among health care professionals. </sec> <sec> <title>OBJECTIVE</title> Our aim is to explore how motivation and ability affect attitudes toward using VCoPs for those working in health care. </sec> <sec> <title>METHODS</title> Data were collected from 86 health care workers using an online survey at two Canadian health care conferences. Participants were shown a mock VCoP and asked about their perceptions of the online platform and related technologies. The survey instrument was developed based on previously validated scales to measure participants’ ability and motivation toward using a VCoP. Attitudes were assessed both at the beginning and end of the study; intention to use the platform was assessed at the end. </sec> <sec> <title>RESULTS</title> Ability (expertise with CoPs and VCoPs) was found to directly affect intention to use the system (<italic>P</italic>&lt;.001 and <italic>P</italic>=.009, respectively) as was motivation (<italic>P</italic>&lt;.001). Argument quality had the greatest effect on formed attitudes toward VCoPs, regardless of the user’s level of experience (lower expertise: <italic>P</italic>=.04; higher expertise: <italic>P</italic>=.003). Those with higher levels of CoPs expertise were also influenced by peripheral cues of source credibility (<italic>P</italic>=.005 for attitude formation and intention to use the system) and connectedness (<italic>P</italic>=.04 for attitude formation; <italic>P</italic>=.008 for intention to use the system), whereas those with lower levels of CoP expertise were not (<italic>P</italic>&gt;.05). A significant correlation between formed attitude and intention to use the VCoPs system was found for those with higher levels of expertise (<italic>P</italic>&lt;.001). </sec> <sec> <title>CONCLUSIONS</title> This research found that both user ability and motivation play an important and positive role in the attitude toward and adoption of health care VCoPs. Unlike previous ELM research, evidence-based arguments were found to be an effective messaging tactic for improving attitudes toward VCoPs for health care professionals with both high and low levels of expertise. Understanding these factors that influence the attitudes of VCoPs can provide insight into how to best design and position such systems to encourage their effective use among health care professionals. </sec>
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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".