SERVICE INTEGRATION IN A HEALTH CARE UNIT: A CASE STUDY
Bibliographic record
Abstract
There are several people to whom I would like to dedicate this project. First of all to my parents whose guidance and support have enabled me to realize many of my dreams. To my sisters for their love of life and their ability to remind me of what is really important in life. To my Grandma who has always believed that I could do anything I put my mind to. Finally, to Ryan whose hugs have carried me through the past year and a half of tears, frustration and laughter. Thank you to each of you. iii This study focuses on the dynamics enabling or constraining radical change in a health care unit in a rural region of Alberta. The unit envisioned change from a fragmented, treatment-based model to an integrative, prevention-based model of health care delivery. This research adopts a case study approach that relies on multiple sources of data including written documents and interviews with groups such as physicians, nurse practitioners (NP), and public health nurses (PHN), who were directly involved in the changes towards integration. The data indicate that a number of institutional and
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".