Emotional journey of wives of spouses diagnosed with bipolar I disorder: moving from vicissitude towards reconciliation
Bibliographic record
Abstract
Purpose Our present study was a qualitative investigation intending to explore the emotional journey of wives whose spouse has been diagnosed with Bipolar I Disorder, using a phenomenological design.Method Semi-structured face to face interviews were conducted with 5 wives of already diagnosed Bipolar I Disorder patients to uncover their lived experience in terms of the emotional journey they had had. For data analysis, we used Hycner’s explicitation process. Moreover, for data verification we employed the strategies of frequent debriefing sessions peer review and member checks.Results Our analysis revealed six major themes encapsulating the participants emotional journey. These included Shock, Betrayal and the Incomprehensible, Apprehensions and Uncertainty, Anger and Irritability, Loneliness and Helplessness, Compassion and Acceptance and Reconciliation.Conclusion It became clear to us that wives of individuals diagnosed with Bipolar I Disorder are on a continuous emotional journey dealing with the burden, stress, complications, uncertainty and making many sacrifices along the way. Our study highlighted many culture specific factors of the phenomenon. This insightful exploration has opened up new horizons to conceptualize the challenges of wives dealing with an ailing spouse in the context of a Pakistani society.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".