Healthcare professionals and managers' participation in developing an intervention: A pre-intervention study in the elderly care context
Bibliographic record
Abstract
BACKGROUND: In order to increase the chances of success in new interventions in healthcare, it is generally recommended to tailor the intervention to the target setting and the target professionals. Nonetheless, pre-intervention studies are rarely conducted or are very limited in scope. Moreover, little is known about how to integrate the results of a pre-intervention study into an intervention. As part of a project to develop an intervention aimed at improving care for the elderly in France, a pre-intervention study was conducted to systematically gather data on the current practices, issues, and expectations of healthcare professionals and managers in order to determine the defining features of a successful intervention. METHODS: A qualitative study was carried out from 2004 to 2006 using a grounded theory approach and involving a purposeful sample of 56 healthcare professionals and managers in Paris, France. Four sources of evidence were used: interviews, focus groups, observation, and documentation. RESULTS: The stepwise approach comprised three phases, and each provided specific results. In the first step of the pre-intervention study, we gathered data on practices, perceived issues, and expectations of healthcare professionals and managers. The second step involved holding focus groups in order to define the characteristics of a tailor-made intervention. The third step allowed validation of the findings. Using this approach, we were able to design and develop an intervention in elderly care that met the professionals' and managers' expectations. CONCLUSION: This article reports on an in-depth pre-intervention study that led to the design and development of an intervention in partnership with local healthcare professionals and managers. The stepwise approach represents an innovative strategy for developing tailored interventions, particularly in complex domains such as chronic care. It highlights the usefulness of seeking out the insight of healthcare professionalnd managers and emphasizes the need to intervene at different levels. Further research will be needed in order to develop a more thorough understanding of the impacts of such strategies on the final outcomes of intervention implementations.
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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".