Governing health regions/informing board members
Bibliographic record
Abstract
Governing boards of Canadian regional health authorities report deficiencies and dissatisfaction with the information available for decision making. Given the importance of health personally and politically the potential impact of deficits is great. The ultimate goal of this study is the improvement of decision support for the governing boards of regionalized and vertically integrated health care systems. Our immediate purpose is to understand how board members use information in decision making. This is required to both model the current communication and information use in decision processes, as well as, to design technology solutions congruent with these. Institutional ethnography was explored as a way of doing systematic inquiry as a preliminary step in the design process. This paper provides a methodological demonstration. Ethnographic fieldwork was conducted with one regional health board. Standard ethnographic data collection methods (observation, key informant interviews, meeting transcripts and documentation) generated data that were analysed using a framework and method developed by Canadian social theorist Dorothy Smith. Taking the standpoint of the decision maker and tracing information links to locations removed in time and space from the decision making environments permits a roadmap of knowledge construction to emerge. Preliminary findings confirm that a sequential linear decision making process is not in evidence. The Board relies on the knowledge and contacts of board members to become concisely informed from sources external to the organization. More extensive information infrastructure is in the planning stages to support the board but much analysis is currently ad hoc. A rich model of the dynamic interplay of work processes, professional discourses, institutional complexes and various knowledge practices, beliefs and ideologies is made visible. The insights gained in this investigation are used as a basis for developing strategies to improve the effective use of information and communication technologies for decision support.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".