Uncertainty and Expectations: Taking Care of a Cardiac Surgery Patient at Home
Bibliographic record
Abstract
Family members of postsurgical patients are, by necessity, taking on the caregiving role sooner without any specified resources to help them. The input of these caregivers is essential so nurses can understand their concerns, needs, and struggles and develop strategies to support the caregivers in their caregiving role. This study was designed to increase nursing knowledge regarding the experiences of being a caregiver of a cardiac surgery patient during the immediate postdischarge period. The qualitative research method of Interpretative Description, first described by Thorne, Kirkham, and MacDonald-Emes in 1997 guided the study. In-depth interviews were held with eight spousal caregivers. Findings revealed that the experience was molded by caregivers' past participation as a caregiver, as well as caregivers' and care recipients' outlook on life, their interpersonal relationship, and their expectations. Caregivers engaged in the process of caregiving that involved being vigilant and monitoring the care recipient's recovery, implementing strategies to assist the recovery process, and taking on a role to provide care and seek help as required. The encounter with caregiving affected all realms of caregivers' lives, and they experienced feelings of stress, vulnerability, and having to put their lives on hold; these feelings were often compounded by uncertainty.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".