Bibliographic record
Abstract
Loosely coupled workgroups are common in the real world, and workers in these groups are autonomous and weakly interdependent. They have patterns of work and collaboration that distinguish them from other types of groups, and groupware systems that are designed to support loose coupling must address these differences. However, they have not been studied in detail in Computer-Supported Cooperative Work (CSCW), and the design process for these groups is currently underspecified. This forces designers to start from scratch each time they develop a system for loosely coupled groups, and they must approach new work settings with little information about how work practices are organized. In this dissertation, I present a design framework to improve the groupware design process for loosely coupled workgroups. The framework has three main parts that add a new layer of support to each of the three stages in the general groupware design process: data collection about the target work setting, analysis of the data, and system design based on the analysis results. The framework was developed to provide designers with support during each of these stages so that they can consider important characteristics of loosely coupled work practice while carrying out design for the target group. The design framework is based on information from CSCW and organizational research, and on real-world design experiences with one type of loosely coupled workgroup—home care treatment teams. The framework was evaluated using observations, interviews, and field trials that were carried out with multidisciplinary home care treatment teams in Saskatoon Health Region. A series of field observations and interviews were carried out with team members from each of the home care disciplines. The framework was then used to develop Mohoc, a groupware system that supports work in home care. Two field trials were carried out where the system was used by teams to support their daily activities. Results were analyzed to determine how well each part of the design framework performed in the design process. The results suggest that the framework was able to fill its role in specializing the general CSCW design process for loosely coupled groups by adding consideration for work and collaboration patterns that are seen in loosely coupled settings. However, further research is needed to determine whether these findings generalize to other loosely coupled workgroups.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".