Alleviating suffering of individuals with multimorbidity and complex needs: A descriptive qualitative study
Bibliographic record
Abstract
BACKGROUND: Individuals living with multimorbidity and/or mental health issues, low education, socioeconomic status, and polypharmacy are often called complex patients. The complexity of their health and social care needs can make them prone to disease burden and suffering. Therefore, they frequently access health care services to seek guidance for managing their illness and suffering. AIMS: The aim of this research was to describe the approaches used by nurses to alleviate the suffering of individuals with multimorbidity and complex needs in acute care settings. RESEARCH DESIGN: A qualitative descriptive approach. PARTICIPANTS AND RESEARCH CONTEXT: Semi-structured interviews were conducted with 19 nurses working in general, medical-surgical, specialized, and intensive care settings across five hospitals in Pakistan. Reflexive thematic analysis was used for analysis. ETHICAL CONSIDERATIONS: Ethical approval was obtained from the Ethical Committee of Al-Nafees Medical College Islamabad, Pakistan. FINDINGS: Four themes were generated: Deeper Exploration of Patients' Health-Illness Situation and Complexity, Prioritizing Patient Psychosocial and Emotional Needs, Instilling Hope and Encouragement in Patients, and Creating a Comforting Environment to Foster Sharing of felt needs. DISCUSSION: Nurses emphasized the need of deeper inquiry into patients illness situation and complexity to discern the impact of determinants on their well-being and develop care plans that are tailored to address psychosocial, emotional, and physical suffering of this patient population. CONCLUSIONS: Alleviation of patient suffering is integral to compassionate nursing care. Nurses use a multifaceted approach entailing sensitive understanding, recognizing sociocultural and structural determinants impact on patient situation, and individual and interdisciplinary altruistic actions to alleviate patient suffering.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".