Evidence and politics of patient experience in Ontario: The perspective of healthcare providers and administrators
Bibliographic record
Abstract
BACKGROUND: Patient experience has a direct impact on patients' engagement in healthcare, their commitment to treatment plans, and their relationship with their healthcare providers, all of which can impact their health outcomes. The complexity of the healthcare system, the increasing health needs of the population, and the priority and knowledge differences among healthcare stakeholders impact how they conceptualize and seek to achieve the ideal patient experience and the weights that they give to different elements of this experience. AIMS: This study sought to understand the perspectives of healthcare providers and administrators in Ontario regarding the factors affecting the patient experience. MATERIALS & METHODS: Qualitative data were collected between April 2018 and May 2019. Twenty-one semi-structured interviews were conducted. Interviewees included physicians, nurses, optometrists, dietitians, quality managers, and policymakers. Thematic analysis was used to analyse the data, utilizing and extending a previously developed patient experience framework. RESULTS: Several themes emerged in the data, and they represent two perspectives on patient experience: the biomedical perspective, which prioritizes health outcomes and gives high weights to healthcare experience factors that can be controlled by healthcare providers, while ignoring other factors, and the sociopolitical perspective, which recognizes the impacts of healthcare politics and the social context of health on patient experience in Ontario. CONCLUSION: The study is timely in light of the current changes in the Ontario healthcare system and the healthcare reform started by the new government, as it sheds light on the possible negative impact of healthcare policy and politics on patient experience.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".