A sociomaterial inquiry into the clinical teaching workplace
Bibliographic record
Abstract
Purpose This paper aims to trace the relational and material ways in which workplace teams come together (or fail to) in the provision of patient care. Design/methodology/approach Six interprofessional scholars brought their unique theoretical and disciplinary lenses to understand the contextualized experiences of the patient and the team. Adopting a critical narrative inquiry (CNR) approach, the experiences of 19 participants were documented as they interacted in the care of an elderly patient over a three-week period. Actor network theory constructs enabled the analysis of multiple artefacts implicated in the interactions to learn of their contribution to the enactment of her care. Findings The study gives empirical insights about ways in which knowledge circulates amongst the workplace and how systemic structures may impede effective and quality patient care. Various types of knowledge are held by different team members, and both individuals and materials (e.g. technologies) can influence the way those knowledges are shared (or not). Research limitations/implications Focusing on a rich data set surrounding one patient documented as theatre serves pedagogical purposes and serves as a shared “boundary-breaking” object to interrogate from multiple stakeholder perspectives. CNR provides for recursive, dynamic learning as readers critically consider experiences within their own contexts. Practical implications Despite research that documents competing political, systemic and economic goals, sedimented policies and practices persist in ways that undermine care goals. Social implications Tackling the urgent issue of an aging population will require expanding collaboration (for planning, research and so on) to include a broader set of stakeholders, including operational, administrative and post-discharge organizations. Attention to social infrastructure as a means to assemble knowledges and improve relationships in the care process is critical. Originality/value Building a boundary-breaking shared object to represent the data offers a unique opportunity for multiple stakeholder groups to enter into dialogue around barriers to workplace interaction and collaboration progress, linking problems to critical perspectives.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.060 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".