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Health impact assessment--how to start the process and make it last.

2003· editorial· en· W2122891860 on OpenAlexaboutno aff
Reiner Banken

Bibliographic record

VenuePubMed · 2003
Typeeditorial
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsHealth impact assessmentPublic healthHealth policyHealth promotionAction (physics)Public relationsInstitutionalisationPopulationPolitical scienceHealth carePopulation healthSocial determinants of healthMedicineEnvironmental healthNursingLaw

Abstract

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Since the Lalonde Report in 1974 on beyond health in Canada, awareness of non-health sector determinants of health has been increasing (1). The World health report 2000 proposed population health as a central objective of health care systems (2) but there are few signs of the concrete mechanisms for intersectoral action that this requires. In 2000, at the Fifth Global Conference for Health Promotion, Mittelmark argued that high-sounding, general calls to improve social responsibility for health are not sufficient to stimulate action. He proposed health impact assessment (HIA) as a device for forcing the relevant bodies to take action in favour of healthy public policies (3). HIA has the potential to catalyse intersectoral action for health by providing information on the foreseeable consequences, both positive and negative, of proposed policies, programmes and projects. To do this, HIA would have to become part of the rules and procedures normally followed by the different decision-making bodies involved. This integration of HIA into the existing procedures has come to be known as institutionalization (4). In this sense entails setting up patterns which condition the perception of interests, obviating some choices and facilitating others (5). After analysing the practice of influencing government decision-making through institutionalized impact analysis, Bartlett concludes: it makes a difference how impact assessment is institutionalized in the policy system; its policy impact is neither simple nor assured. Impact assessment does not influence policy through some magic inherent in its techniques or procedures. More than methodology or substantive focus, what determines the success of impact assessment is the appropriateness and effectiveness in particular circumstances of its implicit policy strategy.(6) What the best strategy is for institutionalizing HIA will depend on the particular political, administrative and economic context of each country. Experience with project HIA has made clear the importance of administrative frameworks for establishing the active practices involved. Legal frameworks for environmental impact assessment (EIA) in many countries already include health impacts as a compulsory element although in practice this is often poorly done. Translating the legal framework into practice seems to require an administrative framework. For example, a memorandum of understanding signed in 1987 in Quebec, Canada, between the Ministry of Health and the Ministry of the Environment has been the key element in the subsequent development of a systematic and active HIA/EIA practice in Quebec. Mutual understanding and trust have been achieved through regular contacts between the professionals in the public health network and those in the Ministry of the Environment (7). For the HIA of policies, the history is still too short to furnish any conclusions as to the role of administrative frameworks, though we can assume that they are important. The policy HIA process which has recently emerged in Quebec as part of a new Public Health Act may provide useful lessons for industrialized countries. The evolving experience in Thailand, described by Phoolcharoen et al. in this issue (pp. 465-467), should be followed closely, as will provide important lessons for institutionalizing HIA in similar contexts. Although institutionalizing HIA seems desirable in order to make a concern for the improvement of health a routine part of decision-making, HIA can become inefficient in a bureaucratic environment. …

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.013
GPT teacher head0.305
Teacher spread0.293 · 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
GenreEditorial

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

Citations16
Published2003
Admission routes1
Has abstractyes

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