The meaning of hope in nursing research: a meta‐synthesis
Bibliographic record
Abstract
The aim of this study was to develop a meta-synthesis of nursing research about hope as perceived by people during sickness and by healthy people. A meta-synthesis does not intend to cover all studies about hope; rather it tries to synthesize qualitative findings from different contexts, cultures and times to provide a global picture of the phenomenon under study. Noblit and Hare's meta-ethnographic approach was used. The approach is a systematic comparison of studies where each study is translated into the other. Data were 15 qualitative studies published in nursing or allied health journals and conducted in USA, Great Britain, Canada, Australia, Norway, Sweden and Finland. The meta-synthesis resulted in six metaphors that illustrate dimensions of hope. These metaphors permeated the experiences of hope regardless of whether the human being was healthy, chronically or terminally ill. They comprise the complexity of hope and were: living in hope, hoping for something, hope as a light on the horizon, hope as a human-to-human relationship, hope vs. hopelessness and fear: two sides of the same coin, and hope as weathering a storm. Knowing the multidimensionality of hope and what hope means from the patient's perspective might help nurses and other healthcare professionals to inspire hope as Florence Nightingale did when she walked with the lamp through the dark corridors and spread hope and light to the patients. We suggest that nurses working with patients with serious conditions such as cancer reflect on the meaning of the metaphors.
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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 | 0.123 | 0.199 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".