Exploring the factors and outcomes of collaborative information monitoring: Findings of a <scp>cross‐case</scp> analysis
Bibliographic record
Abstract
Abstract Collaborative information monitoring (CIM) involves intentional information monitoring activities pursued by a group and could help researchers keep up to date. This paper reports findings of a cross‐case analysis aimed to explore the perceived factors and outcomes of CIM. Seven cases were included in the study, representing 11 members of patient‐oriented research communities (i.e., researchers, trainees, clinicians, research professionals, managers), who have implemented projects in a dedicated CIM system called eSRAP. Data were collected with semistructured interviews, verified with system logs and CIM project documents. Data were analyzed using a deductive/inductive thematic analysis. Cross‐case analysis revealed four types of cases, those that engaged in CIM with eSRAP, without eSRAP, used eSRAP individually (i.e., did not collaborate), or did not collaborate and did not use eSRAP. Analysis confirmed theory‐based types of factors (personal, group, organizational, environmental, information sources, system, task) and outcomes (performance, behavioral, cognitive, affective, relational) and generated new subtypes. The factor specific to cases that engaged in CIM (with or without eSRAP) was group leadership. Specific outcomes were motivation and discussion. Our findings contribute to conceptualizing CIM and can inform practice by providing actionable recommendations for supporting and sustaining CIM projects.
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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 | 0.051 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".