Evaluating the implementation process of a new telerehabilitation modality in three rehabilitation settings using the normalization process theory: study protocol
Bibliographic record
Abstract
Introducing innovations such as telerehabilitation (TR) into routine care involves complex changes in organizations. This study protocol aims to (1) examine the extent to which a TR platform was implemented as intended in three clinical settings and (2) identify which TR activities were becoming integrated into routine clinical practices, and which factors affect the routine use of the platform. A mixed-method prospective single-case study design with multiple embedded units of analysis will be used. Pre/post-implementation data collection will focus on implementation leaders, clinical champions, upper management, and clinical staff. Qualitative data include semistructured individual interviews with leaders, champions, and upper management as well as focus groups with clinical staff who are users and non-users of the TR platform. Quantitative data include TR use data and TR implementation questionnaires. The consolidated framework for implementation research will be used to analyze the implementation process and normalization process theory will be used to analyze the embedding of TR in routine daily practice. The project is expected to yield evidence regarding which specific TR activities are implemented in day-to-day clinical activities as well as capture threats and opportunities to normalization at a critical moment when it is expected to occur.
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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 | 0.050 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".