Demonstrating causality, bestowing honours, and contributing to the arms race: Threats to the sustainability of HPE research
Bibliographic record
Abstract
As the field of health professions education (HPE) continues to evolve, it is necessary to occasionally pause and reflect on the potential effects and outcomes of our research practices. While future-casting does not guarantee that impending negative consequences will be evaded, the exercise can help us avoid pitfalls. In this paper, we reflect on two terms that have taken hold as powerful idols in HPE research that stand above questioning and apart from critique: patient outcomes and productivity. We argue that these terms, and the ways of thinking they uphold, threaten the sustainability of HPE research-one at the level of the community and one at the level of the scholar. First, we suggest that HPE research's history of endorsing a linear and causal association ethos has driven its quest to connect education to patient outcomes. To ensure the sustainability of HPE scholarship, we must deconstruct and disempower patient outcomes as one of HPE's god-terms, as the pinnacle goal of educational activities. To be sustained, HPE research needs to value all of its contributions equally. A second god-term is productivity; it impairs the sustainability of the careers of individual researchers. Problems of honorary authorship, research output expectations, and comparisons with other fields have constructed a space where only scholars with sufficient privilege can prevail. If productivity persists as a god-term, the field of HPE research could decay into a space where new scholars are silenced-not because they fail to make important contributions, but because access is restricted by existing research metrics. These are two of many god-terms threatening the sustainability of HPE research. By highlighting patient outcomes and productivity and by acknowledging our own participation in propagating them, we hope to encourage others to recognize how our collective choices threaten the sustainability of our field.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.142 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".