Exploring the experience of involuntary spousal separation in Ontario, Canada: A Participatory Action Research Study
Bibliographic record
Abstract
Background: In Ontario, the current long-term care (LTC) system continuously fails to support the needs of separated spouses in LTC, leading to adverse effects on the mental health of both residents and their spouses. To support separated spouses, additional research is needed to understand the unique experiences of involuntary separated spouses, as well as exploring separation across different contexts. Objective: The primary aim of this dissertation was to explore spouses’ experiences of involuntary separation in Ontario, Canada. Methods: The design of this dissertation involves two phases. First, a qualitative systematic review using the Joanna Briggs Institute methodology situated this work within evidence pertaining to the impact of spousal separation. Second, a qualitative multi-case study with 11 couples used a participatory action research approach to explore the experience of spousal separation. Results: The Phase I systematic review revealed that loneliness and depression are prevalent among spouses involuntarily separated. However, there was a notable gap in the literature, as researchers often fail to include both spouses as participants, particularly those living in LTC. Additionally, the findings highlight a significant lack of literature investigating the strategies and approaches needed to support separated spouses at the organizational and systemic levels. Phase II of this research identified four overarching themes describing spouses' experiences with separation, including: 1) Spouses face organizational ageism within the policies, procedures and lack of support during their separation, 2) Separated spouses have no voice or inclusion in the LTC decision-making process, 3) Spousal separation is impacted by staff and organizations capacity of care for the spouse living in LTC, and 4) Separated spouses need more education, knowledge, and access to LTC policies and supports. Conclusion: The findings from this dissertation highlight the importance of an inclusive and proactive advocacy approach to improving the services, programs, and policies supporting spouses experiencing involuntary separation. Findings will be used for multiple knowledge dissemination and translation efforts led by participants, including publishing a novel on spousal separation, conducting public talks with stakeholders, researchers, and government officials, and involving older adults in decision-making processes pertaining to spousal separation.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.032 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".