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Record W1988080514 · doi:10.12927/hcpol.2014.23915

What Have We Learned from the Substudies?

2014· article· en· W1988080514 on OpenAlexaffvenue
Raisa Deber

Bibliographic record

VenueHealthcare policy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicPrimary Care and Health Outcomes
Canadian institutionsInstitute of Health Services and Policy Research
Fundersnot available
KeywordsAccountabilityScrutinyPublic relationsQuality (philosophy)Variety (cybernetics)Unintended consequencesBusinessPolitical scienceWork (physics)ConfusionPsychologyLawComputer scienceEngineering

Abstract

fetched live from OpenAlex

The findings suggest that accountability is increasingly seen as important. This scrutiny has both positive and negative implications. It forces providers to be aware of what they are doing, and to strive to improve their performance. Many of our respondents told us that there is an increased focus on quality. Accountability has moved beyond its traditional focus on financial dimensions (and ensuring that resources are not misappropriated) to add a major focus on performance. These are all positive outcomes. Yet our findings also suggest some warnings that should be heeded to ensure these benefits are realized while minimizing adverse unintended consequences. One clear signal arises from the examination of accountability “to whom.” Across many subsectors, but particularly within hospitals and the community, multiple bodies ask for similar information, but in different forms. The resulting costs and confusion can be considerable. A number of the papers in this Special Issue note that informants are concerned that their efforts are being diverted to reporting at the expense of front-line care. A related issue is accountability “by whom.” Complying with accountability requests appears more difficult for smaller organizations, which may not have the resources to respond to increasing requests for information. Respondents begin to see some of these requests as make-work, particularly when they must duplicate efforts for few perceived additional benefits. To the extent that multiple parties are seeking accountability, it would seem advisable for them to coordinate their efforts. As noted in the introduction (Deber 2014), there are a variety of policy goals that can be pursued, among them access, quality (including safety), cost control/cost-effectiveness and customer satisfaction. The accountability models and tools used may focus on various combinations of these. The substudies suggest the importance of clarifying what should be done when conflicts arise among the demands, with different parties stressing different factors. Who should get priority and according to what criteria? Who will resolve conflicts? Do all these demanders of accountability have equal legitimacy? If not, how are they, or should they, be ranked? One clear example arose for professional self-regulation, where demands for transparency may conflict with demands to protect privacy. Another arises from the extent to which goals should be tailored to fit local circumstances. The UK example notes the tension between responding to local and national authorities. Similar tensions were evident in many of the substudies. Another clear finding comes from examining accountability “for what.” The findings from almost all of these substudies suggest the accuracy of our hypothesis, stated in the introduction, that the “production characteristics” of the goods and services being provided indeed have a major impact on accountability. In particular, measurability and controllability appear critical. This finding differs considerably by subsector. The quality of laboratory services, for example, is relatively simple to measure. In contrast, the quality of community-based care is much more difficult to capture in simple metrics, and the accountability metrics used incline heavily towards process measures. A major concern is that factors that are less easy to measure may be ignored, even if they are essential to success. A related concern is that organizations are reluctant to be held accountable for factors they cannot control. Public health units cannot control whether their population smokes, so may attempt to have such indicators removed from the metrics for which they will be held accountable. Cross-system issues showed up as a persistent omission; although ensuring smooth transitions among systems of care is a high priority, it did not turn up in most of the accountability measurement systems that were examined. A particular concern is that respondents in many of the substudies indicated the systems of accountability that had been set up often ignored many aspects they thought were important. This did not mean, however, that respondents thought accountability should be ignored. The bottom line is mixed. Accountability is important; taking steps towards it can help to ensure better-quality care and, ideally, both save resources and improve outcomes. Yet, poorly done, it can divert resources from crucial activities, erode support for what may seem like poorly conducted activities and miss the forest for the trees. We hope that these substudies can be helpful in highlighting strengths and avoiding potential weaknesses.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.374
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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.

Opus teacher head0.186
GPT teacher head0.495
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations4
Published2014
Admission routes2
Has abstractyes

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