Bibliographic record
Abstract
There is something comforting about categorizing objects and events in the world. Categories provide structure to what we see and what we talk about. They are often useful real-world distinctions that extend our capacity to understand and intervene in the world. Scientific instrumentation extends that capacity further, and I believe that scientific publications can do the same. Scientific articles can serve as spring boards for reflection, conception, and intervention in the same way that a telescope can open the skies to our eyes and expand our knowledge of the cosmos; hence, my commitment to JRIPE as an open access journal for the dissemination of peerreviewed research. In this issue, we publish seven new research articles for which I offer the follow ing categorization. The first three articles can be grouped on the basis of their research method. Using a Participatory Action Approach, Huijbregts et al. [1] describe a pilot study of the implementation of a Canadian mental health guideline in a long-term care residence; Baker et al. [2] use Action Research to develop an educational module on Adult Suctioning for multi-professional groups of students; and Brynes et al. [3] report on the development and evaluation of collaboration in three clinical settings in Southeastern Ontario, Canada, using a quasi-experimental research design. The next two articles have their unit of analysis as their most salient aspect. Not that these studies were without method; they used specific research designs to collect data, but their particular distinction was in the target of their analyses; namely, students and their interprofessional learning needs. Baerg et al. [4] explore collab oration learning needs among health professionals, teachers, and students, while Flynn et al. [5] report on differences between Family Medicine Residents and other healthcare learners. The last two studies have common ground in their research settings: rural com munities in Australia. Jacob et al. [6] investigate the perceptions of and opportunities for interprofessional education from the perspectives of staff from three rural health services, and Woodrofe et al. [7] report on three years of results from a mixed methods evaluation of the Australian Interprofessional Rural Health Education Pilot. As both studies seem to suggest, the rural context may be an ideal place to showcase effective interprofessional practice. We will never have an omniscient view of the nature of interprofessional learn ing and practice. We can only have categories and forms of reasoning about it. You will find plenty of both in the articles in this issue. How accurate those forms are is an empirical question which only sustained data collection can answer—more or
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.135 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.029 | 0.033 |
| Insufficient payload (model declined to judge) | 0.021 | 0.018 |
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".