A cyborg ontology in health care: traversing into the liminal space between technology and person‐centred practice
Bibliographic record
Abstract
Person-centred practice indubitably seems to be the antithesis of technology. The ostensible polarity of technology and person-centred practice is an easy road to travel down and in their various forms has been probably travelled for decades if not centuries. By forging ahead or enduring these dualisms, we continue to approach and recede, but never encounter the elusive and the liminal space between technology and person-centred practice. Inspired by Haraway's work, we argue that healthcare practitioners who critically consider their cyborg ontology may begin the process to initiate and complicate the liminal and sought after space between technology and person-centred practice. In this paper, we draw upon Haraway's idea that we are all materially and ontologically cyborgs. Cyborgs, the hybridity of machine and human, are part of our social reality and embedded in our everyday existence. By considering our cyborg ontology, we suggest that person-centred practice can be actualized in the contextualized, embodied and relational spaces of technology. It is not a question of espousing technology or person-centred practice. Such dualisms have been historically produced and reproduced over many decades and prevented us from recognizing our own cyborg ontology. Rather, it is salient that we take notice of our own cyborg ontology and how technological, habitual ways of being may prevent (and facilitate) us to recognize the embodied and contextualized experiences of patients. A disruption and engagement with the habitual can ensure we are not governed by technology in our logics and practices of care and can move us to a conscious and critical integration of person-centred practice in the technologized care environments. By acknowledging ourselves as cyborgs, we can recapture and preserve our humanness as caregivers, as well as thrive as we proceed in our technological way of being.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.116 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".