Measuring Integration of Cancer Services to Support Performance Improvement: The CSI Survey
Bibliographic record
Abstract
Objective: To develop a measure of cancer services integration (CsI) that can inform clinical and administrative decision-makers in their efforts to monitor and improve cancer system performance.Methods: We employed a systematic approach to measurement development, including review of existing cancer/health services integration measures, key-informant interviews and focus groups with cancer system leaders.The research team constructed a Web-based survey that was field-and pilot-tested, refined and then formally HEALTHCARE POLICY Vol.5 No.1, 2009 [37] Measuring Integration of Cancer Services to Support Performance Improvement: The CSI Survey conducted on a sample of cancer care providers and administrators in Ontario, Canada.We then conducted exploratory factor analysis to identify key dimensions of CsI.Results: A total of 1,769 physicians, other clinicians and administrators participated in the survey, responding to a 67-item questionnaire.The exploratory factor analysis identified 12 factors that were linked to three broader dimensions: clinical, functional and vertical system integration.Conclusions: The CsI survey provides important insights on a range of typically unmeasured aspects of the coordination and integration of cancer services, representing a new tool to inform performance improvement efforts. RésuméObjectif : Mettre au point une mesure de l'intégration des services de cancérologie qui permette de renseigner les décideurs cliniques et administratifs dans le suivi et l' amélioration du rendement du réseau de cancérologie.Méthode : Nous avons employé une approche systématique pour la mise au point de mesures, notamment par la revue des mesures actuelles de l'intégration des services de cancérologie et de santé, par des entrevues auprès d'informateurs clés et par des groupes de discussion auprès des dirigeants du réseau de cancérologie.L' équipe de recherche a élaboré un sondage en ligne qui a été testé, précisé puis mené auprès d'un échantillon d' administrateurs et de prestataires de soins de cancérologie en Ontario, au Canada.Nous avons ensuite effectué une analyse factorielle exploratoire afin de déterminer les aspects essentiels de l'intégration des services de cancérologie.Résultats : Au total, 1769 médecins, cliniciens et administrateurs ont répondu au sondage de 67 questions.L' analyse factorielle exploratoire a permis de dégager 12 facteurs qui sont liés à trois aspects généraux : les aspects cliniques, les aspects fonctionnels et les aspects liés à l'intégration systémique verticale.Conclusions : Le sondage sur l'intégration des services de cancérologie donne d'importantes pistes concernant une variété d' aspects habituellement non mesurés en matière de coordination et d'intégration des services de cancérologie, ce qui représente un nouvel outil pour renseigner les initiatives d' amélioration du rendement.T f or more than a decade, health services researchers have focused on the integration of health services as a means to improve performance.measures have been developed that assess both provider-and patient-derived aspects of the coordination and continuity of health services within and across sectors (Gillies et al.
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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".