Supporting and inhibiting factors in recovery experienced by the patients after myocardial infarction during an 18-month follow-up
Bibliographic record
Abstract
Objective: There is a need for studies on recovery as experienced by the patient after myocardial infarction. This study is a report on supporting and inhibiting factors in recovery of Finnish patients after myocardial infarction in an Interpersonal Counselling intervention group and in a control group given standard care at 6 months and 18 months after myocardial infarction. Methods: Firstly, during the 6 months following patients receiving IPC (n=21) and standard care (n=19) kept diaries related to supporting and inhibiting factors in recovery. The data were analyzed by inductive content analysis. Secondly, the patients (44 in the intervention group and 42 in the control group) were interviewed with the same open-ended questions 18 months after myocardial infarction. This data were analyzed using deductive content analysis. An attribution analysis was performed on both data. Results: Five main categories, including supporting and inhibiting factors and subcategories, were identified: (1) clinical and physical, (2) psychological, (3) social, (4) functional and (5) professional. There were no differences between the groups. Conclusions: Recovery experienced by the patient after myocardial infarction seems to consist of many supporting and inhibiting factors. This is important to take into account in developing nursing practice. The topic calls for more specific studies. These results could be useful material in developing and testing a quantitative instrument for more precise measuring of recovery after MI in a randomized setting. To develop theory, the results indicate numerous possibilities to test relationships between supporting and inhibiting factors in recovery after myocardial infarction.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".