Unmet Social Needs of Older Adults With Mobility Limitations Following Inpatient Rehabilitation Discharge in Nigeria: A Qualitative Narrative Inquiry Study
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Approximately, 30% to 60% of older adults experience functional decline following hospitalization, which has implications for their ability to meet social needs after discharge. Exploring the unmet social needs of older adults following discharge is warranted to rethink the elements of hospital discharge in low-resource countries. This study explored the unmet social needs of older adults with mobility limitations following discharge from an inpatient rehabilitation unit in a state hospital in Northern Nigeria. METHODS: This narrative qualitative study is based on the social constructivism paradigm guided by the 3-Dimensional Narrative Inquiry Space (3-D NIS) methodology. We purposively selected and conducted telephone interviews with 12 older adults, aged 55 years and older, with mobility limitations and discharged from inpatient rehabilitation units. The 3-D NIS guided the thematic analysis of the data. Two author coders independently conducted a thematic analysis of the transcripts. Reflexivity was maintained throughout the research process. RESULTS AND DISCUSSIONS: Two related themes [6 sub-themes] emerged: "limitations in social functioning" [leisure activity, religious engagement, and instrumental activities of daily living modifications]; and "accepting the status quo" [Care/support from relatives, friends, and strangers, regrets and aspirations, and adaptation to psychological needs]. Seven tensions from the participants' transcripts were embedded in emotions, focused on belief, well-being, and spirituality, COVID-19 pandemic impact, desire to be "normal," limited finance, non-age-friendly homes and community settings, and undetailed rehabilitation discharge planning. CONCLUSION: This study highlights the substantial unmet social needs of older adults with mobility limitations following discharge from inpatient rehabilitation units in Northern Nigeria, underscoring the importance of a holistic approach to hospital discharge planning. Future research in low-resource countries should delve deeper into developing comprehensive discharge strategies that integrate social workers and rehabilitation professionals to effectively address these multifaceted social needs.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| 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".