Neighbourhood Team Development to promote resident centred approaches in nursing homes: a protocol for a multi component intervention
Bibliographic record
Abstract
BACKGROUND: As the demand for nursing home (NH) services increases, older adults and their families expect exceptional services. Neighbourhood Team Development (NTD) is a multi-component intervention designed to train team members (staff) in the implementation of resident-centered care in NH settings. A neighbourhood is a 32-resident home area within a NH. This paper presents the protocol used to implement and evaluate NTD. The evaluation aimed to 1) examine fidelity with which the NTD was implemented across NHs; 2) explore contextual factors associated with implementation and outcomes of the NTD; and 3) examine effects of NTD on residents, team members, family, and organizational outcomes, and the association between level of implementation fidelity and outcomes. METHODS: The study employed a repeated measure, mixed method design. NTD consisted of a 30-month standardised training and implementation plan to modify the physical environment, organize delivery and services and align staff members to promote inter-professional team collaboration and enhanced resident centeredness. Training was centred in each 32-resident neighbourhood or home area. Quantitative and qualitative data were collected with reliable and valid measures over the course of 3 years from residents (clinical outcomes, quality of life, satisfaction with care, perception of person centeredness, opportunities for social engagement), families (satisfaction with care for relative, person centeredness, relationship opportunities), team members (satisfaction with job, ability to provide person centered care, team relationships) and organizations (retention, turnover, staffing, events) in 6 NHs. Mixed models were used for the analysis. DISCUSSION: The advantages and limitations of the NTD intervention are described. The challenges in implementing and evaluating this multi-component intervention are discussed as related to the complexity of the NH environment. TRIAL REGISTRATION: ClinicalTrials.gov ID: NCT03415217 (January 30, 2018 - Retrospectively registered).
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 | 0.037 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".