Addressing organisational elder abuse using the Participatory Occupational Justice Framework
Bibliographic record
Abstract
The concept of occupational justice and the Occupational Justice Framework are powerful tools in identifying and analyzing injustice sites and situations. These conceptual frameworks are, however, difficult to operationalize concretely against specific occupational injustices experienced by individuals, groups, and communities in specific organisational, sociopolitical, and sociocultural contexts. Conversely, the Participatory Occupational Justice Framework (POJF), grounded in a critical epistemology, proposes collaborative linked steps in order to undertake justice-focussed work. Hence, the POJF was the best conceptual tool to inform and guide this participatory action research (PAR) project which aimed to counter occupational and epistemic injustices faced by older adults who were experiencing organisational abuse in healthcare facilities in Quebec, Canada. This article describes the strength and steps of the POJF in supporting a range of stakeholders as they participated in structurally transformative actions. The latter addresses the contextually bound occupational and epistemic injustices. Specifically, this paper describes a POJF-focussed ethical reflection guide co-created by stakeholders in the PAR that provided a cogent vehicle for organisational changes. We conclude with reflections on key learnings from the PAR process and posit recommendations for future development of the POJF as a tool for organisational and structural reforms.
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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 | 0.110 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.030 | 0.064 |
| Scholarly communication | 0.019 | 0.011 |
| Open science | 0.005 | 0.029 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".