What counts and what is being counted – The social organization of knowledge on the front lines of emergency medical services.
Bibliographic record
Abstract
Paramedics are trained to use specialized medical knowledge and a variety of medical procedures and pharmaceutical interventions to “save patients and prevent further damage” in emergency situations, both as members of “health-care teams” in hospital emergency departments (Swanson, 2005: 96) and on the streets – unstandardized contexts “rife with chaotic, dangerous, and often uncontrollable elements” (Campeau, 2008: 3). The paramedic’s unique skill-set and ability to function in diverse situations have resulted in the occupation becoming ever more important to health care systems (Alberta Health and Wellness, 2008: 12). Today, prehospital emergency services, while varying, exist in every major city and many rural areas throughout North America (Paramedics Association of Canada, 2008) and other countries around the world (Roudsari et al., 2007). Services in North America, for instance, treat and/or transport 2 million Canadians (over 250,000 in Alberta alone ) and between 25 and 30 million Americans annually (Emergency Medical Services Chiefs of Canada, 2006; National EMS Research Agenda, 2001). In Canada, paramedics make up one of the largest groups of health care professionals, with numbers exceeding 20,000 (Pike and Gibbons, 2008; Paramedics Association of Canada, 2008). However, there is little known about the work practices of paramedics, especially in light of recent changes to how their work is organized, making the profession “rich with unexplored opportunities for research on the full range of paramedic work” (Campeau, 2008: 2). This presentation reports on findings from an institutional ethnography that explored the work of paramedics and different technologies of knowledge and governance that intersect with and organize their work practices. More specifically, my tentative focus of this presentation is on discussing some of the ruling discourses central to many of the technologies used on the front lines of EMS in Alberta and the consequences of such governance practices for both the front line workers and their patients. In doing so, I will demonstrate how IE can be used to answer Rankin and Campbell’s (2006) call for additional research into “the social organization of information in health care and attention to the (often unintended) ways ‘such textual products may accomplish…ruling purposes but otherwise fail people and, moreover, obscure that failure’ (p. 182)” (cited in McCoy, 2008: 709).
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.028 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".