The feasibility of the story as a quality instrument as a narrative quality improvement method
Bibliographic record
Abstract
Background and objective: Stories from older adults give insight into their personal lives and in the care they receive. The story as a quality instrument is a narrative quality improvement method with which care professionals can interview older adults about how care is experienced. Each performed interview will be translated into a portrait containing the core themes of the shared story. The objective of this study was to assess the feasibility of and experiences with the story as a quality instrument amongst care professionals and older adults receiving long-term care.Methods: Five care locations providing nursing home care and one providing home care participated in the current study. In total 19 trained care professionals performed interviews with 52 older adults. Both the performed interviews and written portraits were scored according to preset criteria to establish the compliance to the predetermined protocol. Next to that, experiences from care professionals as insider researchers and respondents were gathered.Results: Overall the fidelity for performing the interview was good. In 90% of cases interviewers posed one inviting open question. Following, interviewers used proposed interviewing techniques such as asking in depth questions, asking for an example or summarizing what has been said. In 20 of the interviews, the respondents input accounted for over 80% of the total number of words, and in 27 interviews the respondents’ input accounted for 60%-80%. Fidelity with the protocol for drawing up portraits was sufficient in most cases. In 66% the portrait contained all important themes and in 32% the majority of important themes. One portrait missed a significant proportion of themes mentioned during the interview. The experiences from care professionals consist of successes, challenges, added value and prerequisites.Conclusions: Care professionals were mostly capable of following the method according to protocol after being trained. The method is believed to be a promising innovation because care professionals play a key role in gathering and using stories to improve quality of care. The outcomes can be used by care professionals to learn and improve within their care location according to the quality framework for nursing home care.
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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 | 0.135 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".