When Faces Disappear – A Call for Combining Autoethnography and Complexity Thinking in Future Research on the Intersection of Physical Spaces, Technology and Healthcare Communication Education during COVID
Bibliographic record
Abstract
Autoethnography and complexity thinking should be carefully combined when studying the intersection of physical spaces, technology and healthcare communication education as it took place during COVID. This is the call that stems from a sequential examination of existing literature. Initially, this process identified that existing research tendencies emphasize the need to understand the role physical spaces have in educational processes and highlight the demand to inquire about the opinions of educators regarding these spaces. Subsequently, analysing the manner in which these tendencies are reflected in the literature connected to teaching during COVID, showed that published research demonstrates the evocative potential of educator autoethnographies. Concomitantly, three themes stood out in the examined works: 1. physical spaces form the taken-for-granted skeleton of human existence which can be further explored, 2. matters of communication dominate the literature and set-out many yet to be probed questions and 3. physical spaces, technology and education are undeniably and complexly connected. Altogether, these findings help make the argument that future studies should investigate different instances of the following overarching question: what can be learned about teaching healthcare communication during COVID by using a complexity sensitized autoethnographic investigation focused on the physical spaces-technology-education intersection?
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 | 0.114 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.011 | 0.041 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".