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Record W7029081725

Identification of Re-assessment Intervals to Support a Measurement Based Care (MBC) approach with the interRAI Community Mental Health (CMH) Assessment

2022· dissertation· en· W7029081725 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldComputer Science
TopicHistory of Computing Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthIdentification (biology)Minimum Data SetLife expectancyHealth careResource (disambiguation)Psychological interventionPlan (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

Background: Mental health is a fundamental aspect of people’s health and is a leading cause of disability worldwide. Ineffectively treated mental health problems could result in a shorter life expectancy as a result of death by suicide or serious health problems. These could be improved with a proper treatment plan based on results from systematic assessments. Measuring quality of care is challenging worldwide and varies among organization due to the absence of standardize instruments and lack of ability to regularly collect data within the health care settings. The potential use of the interRAI Community Mental Health (CMH) assessment, as a measurement based care (MBC) instrument, generates data that help to adjust the care planning and resource allocation based on the identified changes in the client’s strengths, preferences, and need. However, further research is needed to identify the optimal re-assessment interval from the initial interRAI CMH assessment. 
\nObjective: This thesis investigated the relationship of time between initial assessment and re-assessment with rates of change in clients’ needs. 
\nMethods: This retrospective study used secondary data from interRAI CMH assessments completed on clients in Ontario, Canada between 2007 and 2020, which are stored on the interRAI Canada server at the University of Waterloo. A variety of statistical techniques were used to identify the shortest period of re-assessment time to see the meaningful rate of changes.
\nResults: This study showed that DSI and PSS-Short scales are valid and reliable over time. The highest rate of change of 75.9% for the DSI was among clients who have been re-assessed within 3-6 months. On average, the rate of change for the DSI was 73.6. The most noticeable rate of change for the PSS-short scale was for clients who have been re-assessed after 6 months: 28.2% between 6-9 months, 25.9% between 9-12 months, and 27.6% after 12 months or more. On average, the rate of change for the PSS-short was 22.7%. The most noticeable rate of change of 14.8% for the traumatic life events CAP was for clients who have been re-assessed after 12 months or more. On average, the rate of change for the traumatic life events CAP was 10.9%. The best rate of improvement (46.7%) after the initial assessment were between 6-9 months and 9-12 months. The period of time when clients worsen their initial score to 20.0% was after 12 months from the initial assessment.
\nConclusion:
\nThrough a thorough analysis of the dataset, this study confirmed that existing re-assessment period of 6 month is appropriate. Understanding the benefits of MBC, specifically the interRAI instruments, in CMH settings makes decision makers to apply standardized measurement instruments to the service delivery to improve quality of care, healthcare outcome, to achieve clients’ goals at the end of the treatment, and to help clinicians to monitor clients’ treatment progress and address their changes appropriately by observing the symptoms on a regular basis.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0040.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.270
Teacher spread0.238 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations0
Published2022
Admission routes1
Has abstractyes

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