“Price to Pay for Being Alive”: Coping with the Post-Operative Demands in Heart Transplantation
Bibliographic record
Abstract
INTRODUCTION: Understanding the problems experienced and coping strategies used after the heart transplantation, health care providers may help heart transplant recipients’ adaptation to the postoperative period and consequently improve their quality of life. However, there is little qualitative evidence on how heart transplant recipients develop coping strategies and adapt to postoperative life.AIM: The aims of the study were a) to identify the physical and psychosocial problems experienced by heart transplant recipients and b) to identify coping strategies used by heart transplant recipients.METHODS: A grounded theory research approach was used in data collection and analysis for studying heart transplant recipients’ experience. The participants in this study were 42 heart transplant recipients. The data were gathered by taped, unstructured, in-depth interviews. Constant comparison analysis was used to interpret the data.FINDINGS: Four categories developed from the data analysis, namely: “traumatic experience” “that’s a small price to pay for being alive” “somebody else’s heart inside me” and “coping”. The central or core category, “That’s a small price for being alive Vs Too big a price to pay for being alive” emerged and was the main theme around which other categories were integrated. Further interpretation of findings led to the development of a theory entitled “Price to pay for being alive: Coping with the post-operative demands in Heart Transplantation”.CONCLUSION: The theory developed within the frame of this study offers an extension of Moos’ Crisis Theory, and applies his basic concepts of major life crises and transitions with adaptations, to heart transplant patients. One such adaptation is the assertion that the factors that influence how adaptively an individual copes with a heart transplant crisis are different from any other health crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".