A model to develop compassionate relationship-centred care between older people, relatives and staff identifies seven factors necessary to promote appreciative caring conversations
Bibliographic record
Abstract
Commentary on: Dewar B, Nolan M. Caring about caring: developing a model to implement compassionate relationship centred care in an older people care setting. Int J Nurs Stud 2013;50:1247–58.[OpenUrl][1][CrossRef][2][PubMed][3] A longstanding concern regarding quality of care for older people in acute care hospital settings exists. The authors of this study point out the tension between efficient patient care activities, and development of therapeutic carer–patient relationships, a strain that may cause compassionate care to be lost. They established a link between thearpeutic relationships, quality of care and patient and staff satisfaction. Investigators sought to discover the dimensions of compassionate care in the … [1]: {openurl}?query=rft.jtitle%253DInt%2BJ%2BNurs%2BStud%26rft.volume%253D50%26rft.spage%253D1247%26rft_id%253Dinfo%253Adoi%252F10.1016%252Fj.ijnurstu.2013.01.008%26rft_id%253Dinfo%253Apmid%252F23427893%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/j.ijnurstu.2013.01.008&link_type=DOI [3]: /lookup/external-ref?access_num=23427893&link_type=MED&atom=%2Febnurs%2F17%2F4%2F123.atom
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".