Patients with heart failure had inadequate information about the disease and lacked the tools for optimal self care
Bibliographic record
Abstract
Horowitz CR, Rein SB, Leventhal H. A story of maladies, misconceptions and mishaps: effective management of heart failure. Soc Sci Med 2004;58:631–43.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q How do patients with congestive heart failure (CHF) perceive and understand the disease and self care? Grounded theory. An urban, academic, tertiary care hospital in the US. 19 patients (age range 52–89 y, 53% men) treated for CHF in the hospital, emergency department (ED), or internal medicine or cardiology clinics were identified from a database of inpatient and ambulatory encounters for CHF. Patients participated in audiotaped semistructured interviews (mean duration 50 min), which were transcribed verbatim. Questions focused on patients’ illness perspectives, self care, help seeking behaviour, attitudes toward physicians, access to care, definition of and reaction to worsening of their condition, and a detailed description of their most recent critical episode of CHF, if one occurred. Dominant themes were identified by the constant comparative method and compared with the “common sense” model of illness. 3 dominant themes emerged. (1) Inadequate knowledge of the causes, symptoms, and consequences of CHF (gaps in depth and breadth). Patients did not connect CHF or a “weak heart” to … [1]: {openurl}?query=rft.jtitle%253DSocial%2Bscience%2B%2526%2Bmedicine%26rft.stitle%253DSoc%2BSci%2BMed%26rft.aulast%253DHorowitz%26rft.auinit1%253DC.%2BR.%26rft.volume%253D58%26rft.issue%253D3%26rft.spage%253D631%26rft.epage%253D643%26rft.atitle%253DA%2Bstory%2Bof%2Bmaladies%252C%2Bmisconceptions%2Band%2Bmishaps%253A%2Beffective%2Bmanagement%2Bof%2Bheart%2Bfailure.%26rft_id%253Dinfo%253Adoi%252F10.1016%252FS0277-9536%252803%252900232-6%26rft_id%253Dinfo%253Apmid%252F14652059%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1016/S0277-9536(03)00232-6&link_type=DOI [3]: /lookup/external-ref?access_num=14652059&link_type=MED&atom=%2Febnurs%2F7%2F4%2F127.atom [4]: /lookup/external-ref?access_num=000187743300017&link_type=ISI
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".