“Is My Heart Healing?” A Meta-synthesis of Patients' Experiences After Acute Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: Recovery from acute myocardial infarction (AMI) has been primarily understood in a narrow medical sense. For patients who survive, secondary prevention focuses largely on enhancing clinical outcomes. As a result, there is a lack of descriptive accounts of patients' experiences after AMI and little is known about how people go about the challenge of recovering from such an event. OBJECTIVE: We conducted a meta-synthesis of the available literature on qualitative accounts of patients' experiences after AMI. METHODS: We searched for relevant papers that were descriptive, qualitative accounts of participants' experiences after AMI across 4 electronic databases (April 2016). Using an adapted meta-ethnography approach, we analyzed the findings by translating studies into one another and synthesizing the findings from the studies. RESULTS: After a review of titles/abstracts, reading each article twice in full, and cross-referencing articles, this process resulted in 17 studies with 224 participants (48% women) aged 23 to 90 years. All participants provided a first-person account of an AMI within the 3-day to 25-year time frame. Two major themes emerged that characterized patients' experiences: navigating lifestyle changes and navigating the emotional reaction to the event-consisting of various subthemes. CONCLUSION: Although AMI tends to be seen as a discrete event, participants are left with little professional guidance as to how to negotiate significant, and often discordant, psychosocial changes that have long-lasting effects on their lives, similar to persons with chronic illnesses but without research in place to figure out how to best support them.
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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 | 0.066 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".